KGS

Thesis Proposal: Uncertainty in Knowledge Graph Embeddings (2025.naacl-srw)

Challenge:	Knowledge graphs (KGs) are incomplete and miss some information.
Approach:	They propose to learn entity representations via a graph structure that uses Seen-entities, Unseen-Entities and words as nodes created from the descriptions of all entities.
Outcome:	The proposed method improves relation prediction for the entity pairs containing Unseen-entities.

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Yuqicheng Zhu

Knowledge Graphs meet Moral Values (2020.starsem-1)

Challenge:	Knowledge graph embedding (KGE) methods map entities and relations from knowledge graphs into numerical vector spaces.
Approach:	They propose to investigate various types of uncertainty in knowledge graph embedding methods and explore strategies to quantify, mitigate, and reason under uncertainty effectively.
Outcome:	The proposed methods have shown to be reliable in high-stakes domains and provide greater confidence in their use beyond benchmark datasets.

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Ioana Hulpuș, Jonathan Kobbe, Heiner Stuckenschmidt, Graeme Hirst

COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation (2021.naacl-demos)

Challenge:	Moral Foundations Theory (MFT) is one of the most adopted theories of morality due to its accompanying lexicon, the Moral Foundation Dictionary (MFD).
Approach:	They propose to use the Moral Foundation Dictionary to analyze moral values in three widely used KGs and propose several Personalized PageRank variations to score concepts and entities in the KG with respect to their relevance to the different moral values.
Outcome:	The proposed methods help to operationalize morality in both NLP and KG communities.

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Qingyun Wang, Manling Li, Xuan Wang, Nikolaus Parulian, Guangxing Han, Jiawei Ma, Jingxuan Tu, Ying Lin, Ranran Haoran Zhang, Weili Liu, Aabhas Chauhan, Yingjun Guan, Bangzheng Li, Ruisong Li, Xiangchen Song, Yi Fung, Heng Ji, Jiawei Han, Shih-Fu Chang, James Pustejovsky, Jasmine Rah, David Liem, Ahmed ELsayed, Martha Palmer, Clare Voss, Cynthia Schneider, Boyan Onyshkevych

Improving Knowledge Graph Embedding Using Simple Constraints (P18-1)

Challenge:	a new framework to digest relevant biomedical knowledge is needed to combat COVID-19 . quantity of research results is a bottleneck, and false information promoted in publications .
Approach:	a team of researchers has developed a framework to extract multimedia knowledge elements from scientific literature to combat COVID-19.
Outcome:	a new framework extracts fine-grained multimedia knowledge elements from scientific literature . it provides detailed contextual sentences, subfigures, and knowledge subgraphs as evidence . the framework is based on a case study of drug repurposing .

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Boyang Ding, Quan Wang, Bin Wang, Li Guo

Noise-powered Multi-modal Knowledge Graph Representation Framework (2025.coling-main)

Challenge:	Recent efforts focused on designing more complicated models or incorporating extra information beyond triples.
Approach:	They propose to use non-negativity constraints on entity representations and approximate entailment constraints on relation representations to improve KG embedding.
Outcome:	The proposed model outperforms baseline models on WordNet, Freebase, and DBpedia.

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Zhuo Chen, Yin Fang, Yichi Zhang, Lingbing Guo, Jiaoyan Chen, Jeff Z. Pan, Huajun Chen, Wen Zhang

KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation (2021.tacl-1)

Challenge:	Current efforts to integrate MMKG with pretraining are scarce.
Approach:	They propose a method that integrates multi-modal entity features into MMKGs using a Transformer-based architecture equipped with modality-level noise masking.
Outcome:	The proposed method achieves SOTA performance across ten datasets.

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Xiaozhi Wang, Tianyu Gao, Zhaocheng Zhu, Zhengyan Zhang, Zhiyuan Liu, Juanzi Li, Jian Tang

Deep Cognitive Reasoning Network for Multi-hop Question Answering over Knowledge Graphs (2021.findings-acl)

Challenge:	Existing language representation models (PLMs) cannot capture factual knowledge from text.
Approach:	They propose a unified model for Knowledge Embedding and Pre-trained LanguagERepresentation which integrates factual knowledge into PLMs and produces effective text-enhanced KE with the strong PLM.
Outcome:	The proposed model improves on existing pre-trained language representation models and improves their performance on various NLP tasks.

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Jianyu Cai, Zhanqiu Zhang, Feng Wu, Jie Wang

Temporal Fact Reasoning over Hyper-Relational Knowledge Graphs (2024.findings-emnlp)

Challenge:	Knowledge Graphs (KGs) store structured human knowledge with nodes and edges being entities and relations between them.
Approach:	They propose a deep cognitive reasoning network that uses two phases to find answers in large candidate entity sets.
Outcome:	The proposed method significantly outperforms state-of-the-art methods on benchmark datasets.

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Zifeng Ding, Jingcheng Wu, Jingpei Wu, Yan Xia, Bo Xiong, Volker Tresp

DBee: A Database for Creating and Managing Knowledge Graphs and Embeddings (D19-53)

Challenge:	Existing models of temporal fact reasoning do not explicitly specify temporal information for each fact.
Approach:	They propose a new type of data structure called hyper-relational TKG to study temporal fact reasoning over HKGs.
Outcome:	The proposed model is based on two new benchmark HTKG datasets . it provides additional key-value pairs (i.e., qualifiers) for each KG fact .

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Viktor Schlegel, André Freitas

Jointly Learning Entity and Relation Representations for Entity Alignment (D19-1)

Challenge:	DBee provides a data model which operates over knowledge graphs and embedding vector spaces .
Approach:	They describe a database which provides a data model which exploits the semantic properties of large-scale knowledge graphs and embedding vector spaces.
Outcome:	The proposed model exploits the semantic properties of both types of representations.

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Yuting Wu, Xiao Liu, Yansong Feng, Zheng Wang, Dongyan Zhao

Leveraging LLM-GNN Integration for Open-World Question Answering over Knowledge Graphs (2026.eacl-long)

Challenge:	Entity alignment is a viable method for integrating heterogeneous knowledge among different knowledge graphs (KGs).
Approach:	They propose a Graph Convolutional Network-based framework for learning relation representations by embedding relation seeds into entities and incorporating relation approximation into entities to iteratively improve alignment.
Outcome:	The proposed approach outperforms state-of-the-art methods on three real-world cross-lingual datasets.

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Hussein Abdallah, Ibrahim Abdelaziz, Panos Kalnis, Essam Mansour

MEKER: Memory Efficient Knowledge Embedding Representation for Link Prediction and Question Answering (2022.acl-srw)

Challenge:	Traditional KGQA assumes a closed world where answers must exist in the KG, limiting real-world applicability.
Approach:	They propose a system that combines a pre-trained GNN and an LLM for open-world QA.
Outcome:	The proposed system outperforms existing LLM–GNN systems on standard benchmarks and GLOW-BENCH, achieving up to 53.3% and an average 38% improvement.

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Viktoriia Chekalina, Anton Razzhigaev, Albert Sayapin, Evgeny Frolov, Alexander Panchenko

PipeNet: Question Answering with Semantic Pruning over Knowledge Graphs (2024.starsem-1)

Challenge:	Existing methods to embed learning use a standard Neural Networks (NN) backward mechanism, duplicating its memory consumption.
Approach:	They propose a memory-efficient KG embedding model that embeds knowledge graphs as 3rd-order binary tensors.
Outcome:	The proposed model yields comparable performance on link prediction and KG-based question answering tasks.

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Ying Su, Jipeng Zhang, Yangqiu Song, Tong Zhang

Constraint-based Multi-hop Question Answering with Knowledge Graph (2022.naacl-industry)

Challenge:	Existing approaches to utilizing explicit knowledge graphs (KGs) are limited by the number of nodes in the subgraph.
Approach:	They propose a grounding-pruning-reasoning pipeline to prune noisy nodes in subgraphs to improve the efficiency of graph reasoning with KG.
Outcome:	The proposed method reduces computation cost and memory usage while obtaining decent representation of pruned subgraphs.

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Sayantan Mitra, Roshni Ramnani, Shubhashis Sengupta

Guiding Neural Entity Alignment with Compatibility (2022.emnlp-main)

Challenge:	Recent work addresses multi-hop KGQA, which requires reasoning across numerous edges of the KG.
Approach:	They propose to use KG embeddings to reduce KG sparsity by performing missing link prediction.
Outcome:	Empirical results show that the proposed method produces state-of-the-art results on three KGQA datasets.

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Bing Liu, Harrisen Scells, Wen Hua, Guido Zuccon, Genghong Zhao, Xia Zhang

Conformalized Answer Set Prediction for Knowledge Graph Embedding (2025.naacl-long)

Challenge:	Entity Alignment (EA) aims to find equivalent entities between two Knowledge Graphs (KGs) labelled data is used to learn neural EA models, but this aspect is neglected .
Approach:	They propose a framework to integrate compatibility into neural EA models . they aim to find equivalent entities between two Knowledge Graphs (KGs)
Outcome:	The proposed framework can achieve comparable effectiveness with supervised training using 20% of labelled data.

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Yuqicheng Zhu, Nico Potyka, Jiarong Pan, Bo Xiong, Yunjie He, Evgeny Kharlamov, Steffen Staab

Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks (D18-1)

Challenge:	Knowledge graph embeddings (KGE) map entities and predicates into numerical vectors, providing non-classical reasoning capabilities based on similarities and analogies between entities and relations.
Approach:	They propose to use knowledge graph embeddings to provide non-classical reasoning capabilities by exploiting similarities and analogies between entities and relations.
Outcome:	The proposed model can generate answer sets with probabilistic guarantees on four benchmark datasets and is scaled well with respect to the difficulty of the query.

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Zhichun Wang, Qingsong Lv, Xiaohan Lan, Yu Zhang

BUCA: A Binary Classification Approach to Unsupervised Commonsense Question Answering (2023.acl-short)

Challenge:	Existing approaches to align multilingual knowledge graphs with counterparts in different languages are not effective.
Approach:	They propose a novel approach for cross-lingual KG alignment via graph convolutional networks . they train GCNs to embed entities of each language into a unified vector space .
Outcome:	The proposed approach gets the best performance on real multilingual KGs compared with other embedding-based approaches.

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Jie He, Simon U, Victor Gutierrez-Basulto, Jeff Pan

Information Retrieval

Challenge:	Existing methods for commonsense reasoning use knowledge graphs to train models . however, it is not always possible to have relevant training data available .
Approach:	They propose to transform a question-answer task into a binary classification task by ranking all candidate answers according to their reasonableness.
Outcome:	The proposed approach is less data hungry than existing methods using KGs.

KGxBoard: Explainable and Interactive Leaderboard for Evaluation of Knowledge Graph Completion Models (2022.emnlp-demos)

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Haris Widjaja, Kiril Gashteovski, Wiem Ben Rim, Pengfei Liu, Christopher Malon, Daniel Ruffinelli, Carolin Lawrence, Graham Neubig

Link Prediction on N-ary Relational Facts: A Graph-based Approach (2021.findings-acl)

Challenge:	Knowledge Graphs (KGs) store information in the form of (head, predicate, tail)-triples.
Approach:	They propose a framework for performing fine-grained evaluation on meaningful subsets of data.
Outcome:	The proposed framework tests models on meaningful subsets of the data, which would have been impossible to detect with standard averaged single-score metrics.

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Quan Wang, Haifeng Wang, Yajuan Lyu, Yong Zhu

Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment (2022.acl-long)

Challenge:	Existing work on knowledge graphs (KGs) focused on binary relations, but higher-arity relations are ubiquitous in real-world KGs.
Approach:	They propose a graph-based approach to link prediction on knowledge graphs using n-ary relational facts and edge-biased fully-connected attention.
Outcome:	The proposed approach performs substantially better than current state-of-the-art across a variety of n-ary relational benchmarks.

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Zijie Huang, Zheng Li, Haoming Jiang, Tianyu Cao, Hanqing Lu, Bing Yin, Karthik Subbian, Yizhou Sun, Wei Wang

Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation (2022.findings-emnlp)

Challenge:	Existing methods to predict missing facts in knowledge graphs are limited in language alignment . SS-AGA uses seed alignment as an edge type to fuses all KGs as a whole graph .
Approach:	They propose a self-supervised adaptive graph alignment method that fuses all KGs as a whole graph by regarding alignment as 'a new edge type' they propose SS-AGA method that uses relation-aware attention weights to capture potential alignment pairs in a new paradigm.
Outcome:	The proposed method can predict missing facts in a knowledge graph (KG) but language alignment is scarce and new alignment identification is noisy.

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Kai Zhang, Yu Wang, Hongyi Wang, Lifu Huang, Carl Yang, Xun Chen, Lichao Sun

Thesis Proposal: A Multi-Agent System for Ontology-Based Perspective-Aware Knowledge Extraction (2026.eacl-srw)

Challenge:	Existing frameworks that share entity embeddings of knowledge graphs (KGs) would incur a severe privacy leakage.
Approach:	They propose a new attack method that aims to recover the original embedding information based on the known entity embeddables of FedE.
Outcome:	The proposed framework can be used to infer whether a specific relation exists in a private client.

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Luiz Do Valle Miranda, Grzegorz J. Nalepa

A Decade of Knowledge Graphs in Natural Language Processing: A Survey (2022.aacl-main)

Challenge:	Current AI systems consolidate multiple perspectives into singular, decontextualized schemas, introducing representational bias and information loss.
Approach:	They propose a framework to operationalize perspective-aware knowledge extraction using ontologies and Large Language Models.
Outcome:	The proposed framework can operationalize perspective-aware knowledge extraction without representational bias and information loss.

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Phillip Schneider, Tim Schopf, Juraj Vladika, Mikhail Galkin, Elena Simperl, Florian Matthes

StATIK: Structure and Text for Inductive Knowledge Graph Completion (2022.findings-naacl)

Challenge:	Knowledge graphs (KGs) are a representation of semantic relations between entities . despite their popularity, there is still no general understanding of what exactly a KG is or for what tasks it is applicable.
Approach:	They analyze 507 papers on knowledge graphs in natural language processing (NLP) they provide a taxonomy of tasks and review the maturity of individual research streams .
Outcome:	The findings summarize the literature and highlight directions for future work.

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Elan Markowitz, Keshav Balasubramanian, Mehrnoosh Mirtaheri, Murali Annavaram, Aram Galstyan, Greg Ver Steeg

GammaE: Gamma Embeddings for Logical Queries on Knowledge Graphs (2022.emnlp-main)

Challenge:	Knowledge graphs (KGs) represent incomplete knowledge bases.
Approach:	They propose to use language models to extract semantic information from text descriptions while using Message Passing Neural Networks to capture structural information.
Outcome:	The proposed model achieves state of the art on three challenging inductive baselines.

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Dong Yang, Peijun Qing, Yang Li, Haonan Lu, Xiaodong Lin

Conversational Question Answering with Language Models Generated Reformulations over Knowledge Graph (2024.findings-acl)

Challenge:	Existing methods for embedding knowledge graphs are difficult due to complicated query structures and incomplete graph data.
Approach:	They propose a probabilistic embedding model for encoding entities and queries to answer different types of FOL queries on KGs.
Outcome:	The proposed model outperforms state-of-the-art models on public benchmarks on three large logical query datasets.

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Lihui Liu, Blaine Hill, Boxin Du, Fei Wang, Hanghang Tong

KGPool: Dynamic Knowledge Graph Context Selection for Relation Extraction (2021.findings-acl)

Challenge:	State-of-the-art ConvQA methods struggle with inexplicit question-answer pairs, which can degrade ConvQ performance.
Approach:	They propose a reinforcement learning based model, CoRnNet, which utilizes question reformulations generated by large language models to improve ConvQA performance.
Outcome:	The proposed model outperforms state-of-the-art ConvQA models by using question reformulations generated by large language models (LLMs).

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Abhishek Nadgeri, Anson Bastos, Kuldeep Singh, Isaiah Onando Mulang’, Johannes Hoffart, Saeedeh Shekarpour, Vijay Saraswat

LightEA: A Scalable, Robust, and Interpretable Entity Alignment Framework via Three-view Label Propagation (2022.emnlp-main)

Challenge:	Existing methods for relation extraction (RE) use only expanded facts from the knowledge graph .
Approach:	They propose a method for relation extraction from a single sentence . they use a neural network to expand the context with additional facts from the KG .
Outcome:	The proposed method is more accurate than state-of-the-art methods on standard datasets.

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Xin Mao, Wenting Wang, Yuanbin Wu, Man Lan

Mitigating Hallucination by Integrating Knowledge Graphs into LLM Inference – a Systematic Literature Review (2025.acl-srw)

Challenge:	Existing EA methods inherit the inborn defects from their neural network lineage: poor interpretability and weak scalability.
Approach:	They propose a neural-free EA framework that can find equivalent entity pairs between KGs.
Outcome:	The proposed framework has impressive scalability, robustness, and interpretability.

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Robin Wagner, Emanuel Kitzelmann, Ingo Boersch

Fact-Tree Reasoning for N-ary Question Answering over Knowledge Graphs (2022.findings-acl)

Challenge:	Large Language Models (LLMs) have made significant progress on different language tasks, but they tend to "hallucinate" plausible but factually incorrect answers.
Approach:	They propose to integrate knowledge graphs (KGs) into LLM inference to reduce hallucinations by searching online and applying a selection process.
Outcome:	The proposed integration improves performance on benchmark datasets and also to mitigate hallucinations.

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Yao Zhang, Peiyao Li, Hongru Liang, Adam Jatowt, Zhenglu Yang

The Role of Exploration Modules in Small Language Models for Knowledge Graph Question Answering (2025.acl-srw)

Challenge:	Current Question Answering over Knowledge Graphs (KGQA) tasks focus on binary facts, but neglect n-ary facts.
Approach:	They propose a new fact-tree reasoning framework that transforms the question into a fact tree and performs iterative fact reasoning on the fact tree to infer the correct answer.
Outcome:	The proposed framework performs iterative fact reasoning on the fact tree to infer the correct answer.

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Yi-Jie Cheng, Oscar Chew, Yun-Nung Chen

Advancing Abductive Reasoning in Knowledge Graphs through Complex Logical Hypothesis Generation (2024.acl-long)

Challenge:	Existing methods to integrate knowledge graphs into large language models often rely on proprietary or extremely large models .
Approach:	They propose to integrate knowledge graphs into reasoning processes of large language models . they propose to use simple and efficient exploration modules to handle knowledge graph traversal .
Outcome:	The proposed modules improve the performance of small language models on knowledge graph question answering tasks.

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Jiaxin Bai, Yicheng Wang, Tianshi Zheng, Yue Guo, Xin Liu, Yangqiu Song

Question-Aware Knowledge Graph Prompting for Enhancing Large Language Models (2025.findings-acl)

Challenge:	Abductive reasoning is the process of making educated guesses to provide explanations for observations.
Approach:	They propose a task of complex logical hypothesis generation to generate a complex logique hypothesis that can explain a set of observations.
Outcome:	The proposed model generates logical hypotheses closer to the reference hypothesis, but not better on unseen observations.

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Haochen Liu, Song Wang, Chen Chen, Jundong Li

Modeling Multi-mapping Relations for Precise Cross-lingual Entity Alignment (D19-1)

Challenge:	Existing methods for large language models require costly fine-tuning or retrieve noisy KG information.
Approach:	They propose to generate KG-based input embedding prefixes as soft prompts but fail to account for question relevance, resulting in noisy prompts.
Outcome:	The proposed model outperforms state-of-the-art methods across multiple datasets.

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Xiaofei Shi, Yanghua Xiao

A New Pipeline for Knowledge Graph Reasoning Enhanced by Large Language Models Without Fine-Tuning (2024.emnlp-main)

Challenge:	Entity alignment aims to find entities in different knowledge graphs (KGs) that refer to the same real-world object.
Approach:	They propose to use dot product-based functions to define dot products over embeddings to better capture semantics of 1-N, N-1 and N-N relations.
Outcome:	The proposed framework outperforms existing methods on multilingual datasets.

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Zhongwu Chen, Long Bai, Zixuan Li, Zhen Huang, Xiaolong Jin, Yong Dou

CoSe-Co: Text Conditioned Generative CommonSense Contextualizer (2022.naacl-main)

Challenge:	Conventional knowledge Graph Reasoning models learn the embeddings of KG components over the structure of a KG.
Approach:	They propose a pipeline to integrate knowledge from LLMs into KGs without fine-tuning . they propose knowledge alignment, KG reasoning and entity reranking to enhance conventional models .
Outcome:	The proposed pipeline can enhance the performance of conventional KGR models in incomplete and general situations.

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Rachit Bansal, Milan Aggarwal, Sumit Bhatia, Jivat Kaur, Balaji Krishnamurthy

OntoEA: Ontology-guided Entity Alignment via Joint Knowledge Graph Embedding (2021.findings-acl)

Challenge:	Pre-trained language models (PTLMs) have been shown to perform well on natural language tasks.
Approach:	They propose a commonsense contextualizer conditioned on sentences as input to make it generically usable in tasks involving natural language text.
Outcome:	The proposed model improves on existing methods on CSQA, ARC, QASC and OBQA datasets.

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Yuejia Xiang, Ziheng Zhang, Jiaoyan Chen, Xi Chen, Zhenxi Lin, Yefeng Zheng

Contrastive Learning Using Graph Embeddings for Domain Adaptation of Language Models in the Process Industry (2025.emnlp-industry)

Challenge:	Existing methods for aligning knowledge graph entities ignore the ontology which contains critical meta information such as classes and membership relationships with entities.
Approach:	They propose an ontology-guided method where KGs and ontologies are jointly embedded.
Outcome:	Extensive experiments on seven public and industrial benchmarks show the ontology-guided method performs well and is cost-effective.

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Anastasia Zhukova, Jonas Luehrs, Christian Matt, Bela Gipp

Incorporating Global Information in Local Attention for Knowledge Representation Learning (2021.findings-acl)

Challenge:	Recent trends in NLP utilize knowledge graphs to enhance pretrained language models by incorporating additional knowledge from the graph structures to learn domain-specific terminology or relationships between documents that might otherwise be overlooked.
Approach:	They propose to use graph-aware neighborhood contrastive learning methodology SciNCL to enhance pretrained language models by incorporating additional knowledge from graph structures.
Outcome:	The proposed graph-aware neighborhood contrastive learning methodology outperforms a state-of-the-art mE5-large text encoder on the process industry text embedding benchmark while having 3 times fewer parameters.

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Yu Zhao, Han Zhou, Ruobing Xie, Fuzhen Zhuang, Qing Li, Ji Liu

REANO: Optimising Retrieval-Augmented Reader Models through Knowledge Graph Generation (2024.acl-long)

Challenge:	Graph Attention Networks (GATs) are a promising model that takes advantage of localized attention mechanism to perform knowledge representation learning (KRL) on graph-structure data.
Approach:	They propose to incorporate global information into the GAT family of models by using an attention-based global random walk algorithm.
Outcome:	Experimental results on KG entity prediction against the state-of-the-arts demonstrate the effectiveness of the proposed model.

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Jinyuan Fang, Zaiqiao Meng, Craig MacDonald

Learning Low-dimensional Multi-domain Knowledge Graph Embedding via Dual Archimedean Spirals (2024.findings-acl)

Challenge:	Existing knowledge graphs suffer from incompleteness and lack information critical for answering given questions.
Approach:	They propose to enhance the open domain question answering model with a knowledge graph generation module that generates KGs from the passages and an answer predictor.
Outcome:	The proposed model improves the exact match score by 2.7% on the EntityQuestion dataset, with an average improvement of 1.8% across all the datasets.

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Jiang Li, Xiangdong Su, Fujun Zhang, Guanglai Gao

Perform like an Engine: A Closed-Loop Neural-Symbolic Learning Framework for Knowledge Graph Inference (2022.coling-1)

Challenge:	Existing knowledge graph embedding methods make domain constraints on embeddable domains, leading to poor performance.
Approach:	They propose a low-dimensional KGE model for multi-domain knowledge graphs that embeds domains and domains by regularization function.
Outcome:	The proposed model can distinguish entities from domains by encoding the same relation on the same archimedean spiral.

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Guanglin Niu, Bo Li, Yongfei Zhang, Shiliang Pu

ComFact: A Benchmark for Linking Contextual Commonsense Knowledge (2022.findings-emnlp)

Challenge:	Existing knowledge graphs are incomplete and therefore lack interpretability.
Approach:	They propose a closed-loop neural-symbolic learning framework EngineKG to address the natural incompleteness of knowledge graphs.
Outcome:	The proposed model outperforms baselines on link prediction tasks on four real-world datasets.

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Silin Gao, Jena D. Hwang, Saya Kanno, Hiromi Wakaki, Yuki Mitsufuji, Antoine Bosselut

EventKE: Event-Enhanced Knowledge Graph Embedding (2021.findings-emnlp)

Challenge:	Existing methods to retrieve facts from commonsense knowledge graphs are imprecise, requiring heuristics that ignore contexts and ambiguity . a novel benchmark, ComFact, contains 293k in-context relevance annotations for commonsensense triplets .
Approach:	They propose a task of commonsense fact linking where models are given contexts and trained to identify situationally-relevant commonsensical knowledge from KGs.
Outcome:	The proposed benchmark shows that heuristic fact linking approaches are imprecise . however, the models still significantly underperform humans in the commonsense augmentation task .

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Zixuan Zhang, Hongwei Wang, Han Zhao, Hanghang Tong, Heng Ji

ChatKBQA: A Generate-then-Retrieve Framework for Knowledge Base Question Answering with Fine-tuned Large Language Models (2024.findings-acl)

Challenge:	Experimental results show that events can greatly improve the quality of KG embeddings on multiple downstream tasks.
Approach:	They propose an event-enhanced KG embedding model that incorporates events into KGs . they first incorporate event nodes by building a heterogeneous network with event argument links .
Outcome:	The proposed model incorporates event nodes into the original knowledge graphs . it can be used to fuse event information into the KG embeddings on multiple tasks .

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Haoran Luo, Haihong E, Zichen Tang, Shiyao Peng, Yikai Guo, Wentai Zhang, Chenghao Ma, Guanting Dong, Meina Song, Wei Lin, Yifan Zhu, Anh Tuan Luu

ERNIE: Enhanced Language Representation with Informative Entities (P19-1)

Challenge:	Existing KBQA methods address inefficient knowledge retrieval and semantic parsing errors.
Approach:	They propose a generatethen-retrieve KBQA framework that generates logical form and replaces entities and relations with an unsupervised retrieval method to improve both generation and retrieval more directly.
Outcome:	Experimental results show that ChatKBQA achieves new state-of-the-art performance on standard KBQA datasets, WebQSP, and CWQ.

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Zhengyan Zhang, Xu Han, Zhiyuan Liu, Xin Jiang, Maosong Sun, Qun Liu

From Graph to Text and Back: Semantic Fidelity in Automated Industrial Knowledge Graphs (2026.acl-industry)

Challenge:	Existing pre-trained language models rarely consider incorporating knowledge graphs (KGs) Existing models capture rich semantic patterns from plain text and can be fine-tuned to improve performance of NLP tasks.
Approach:	They propose to incorporate knowledge graphs into pre-trained language models to enhance language representation with external knowledge.
Outcome:	The proposed model can take full advantage of lexical, syntactic, and knowledge information simultaneously.

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Kamyar Zeinalipour, Silvia Severini, Alessia Borghini, Sara Cardarelli, Marco Maggini, Marco Gori

Worldly Wise (WoW) - Cross-Lingual Knowledge Fusion for Fact-based Visual Spoken-Question Answering (2021.naacl-main)

Challenge:	Large Language Models (LLMs) often hallucinate entities or omit relations, posing unacceptable liability.
Approach:	They propose a self-supervised round-trip pipeline to enforce strict semantic fidelity in KG-to-text generation.
Outcome:	The proposed approach improves triple-extraction accuracy and verbalization faithfulness without manual annotation or massive teacher models.

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Kiran Ramnath, Leda Sari, Mark Hasegawa-Johnson, Chang Yoo

Multi-Task Learning for Knowledge Graph Completion with Pre-trained Language Models (2020.coling-main)

Challenge:	Question-Answering has long been of interest, but its accessibility to users through a speech interface and its support to multiple languages have not been addressed in prior studies.
Approach:	They propose a task and a synthetically-generated dataset to do Fact-based Visual Spoken-Question Answering (FVSQA) the task requires a system to retrieve an entity from Knowledge Graphs (KGs) the question is spoken rather than typed.
Outcome:	The proposed task performs at same levels of accuracy across 3 languages, including English, Hindi, and Turkish.

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Bosung Kim, Taesuk Hong, Youngjoong Ko, Jungyun Seo

SimGRAG: Leveraging Similar Subgraphs for Knowledge Graphs Driven Retrieval-Augmented Generation (2025.findings-acl)

Challenge:	Existing knowledge graph completion methods are lacking in ranking metrics such as Hits@k . despite the high performance, the proposed method is still behind state-of-the-art models.
Approach:	They propose a multi-task learning method that integrates relational and relevance ranking tasks with target link prediction to improve ranking performance.
Outcome:	The proposed method improves ranking performance but still behind state-of-the-art models in Hits@k and Mean Rank metrics.

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Yuzheng Cai, Zhenyue Guo, YiWen Pei, WanRui Bian, Weiguo Zheng

HyperCL: A Contrastive Learning Framework for Hyper-Relational Knowledge Graph Embedding with Hierarchical Ontology (2024.findings-acl)

Challenge:	Recent advances in large language models (LLMs) have shown impressive versatility across various tasks.
Approach:	They propose a retrieval-augmented generation method that integrates LLMs with external knowledge sources to produce grounded outputs.
Outcome:	The proposed method outperforms state-of-the-art KG-driven methods in question answering and fact verification.

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Yuhuan Lu, Weijian Yu, Xin Jing, Dingqi Yang

Random Entity Quantization for Parameter-Efficient Compositional Knowledge Graph Representation (2023.emnlp-main)

Challenge:	Existing studies neglect the ontology of knowledge Graph (KG) embeddings and suffer from the dominance issue of facts over ontologies.
Approach:	They propose a framework for hyper-relational KG embeddings that captures the hierarchical ontology and a concept-aware contrastive loss to alleviate the dominance issue.
Outcome:	The proposed framework improves on three real-world datasets and shows that it can integrate with other embedding methods and improve link prediction performance.

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Jiaang Li, Quan Wang, Yi Liu, Licheng Zhang, Zhendong Mao

A Simple Temporal Information Matching Mechanism for Entity Alignment between Temporal Knowledge Graphs (2022.coling-1)

Challenge:	Existing approaches to learning on Knowledge Graphs (KGs) are not critical for learning on KGs.
Approach:	They propose an alternative approach to represent entities by composing entity-corresponding codewords matched from predefined small-scale codebooks.
Outcome:	The proposed approach achieves similar results to existing methods.

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Li Cai, Xin Mao, Meirong Ma, Hao Yuan, Jianchao Zhu, Man Lan

BanglaAutoKG: Automatic Bangla Knowledge Graph Construction with Semantic Neural Graph Filtering (2024.lrec-main)

Challenge:	Existing methods for EA between temporal KGs incorporate relational and temporal information into entity embeddings.
Approach:	They propose a method to generate unsupervised alignment seeds using temporal information from TKGs.
Outcome:	The proposed method outperforms the previous methods by using temporal information.

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Azmine Toushik Wasi, Taki Hasan Rafi, Raima Islam, Dong-Kyu Chae

PaperRobot: Incremental Draft Generation of Scientific Ideas (P19-1)

Challenge:	Bangla is underrepresented in KGs due to lack of comprehensive datasets, encoders, NER models, part-of-speech taggers, and lemmatizers.
Approach:	Bangla is underrepresented in KGs due to lack of comprehensive datasets, encoders, NER models, part-of-speech taggers, and lemmatizers. authors propose a framework that can automatically construct Bengali KG from any Bangla text.
Outcome:	The proposed framework can automatically construct Bengali KGs from any Bangla text.

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Qingyun Wang, Lifu Huang, Zhiying Jiang, Kevin Knight, Heng Ji, Mohit Bansal, Yi Luan

Neural-based Mixture Probabilistic Query Embedding for Answering FOL queries on Knowledge Graphs (2022.emnlp-main)

Challenge:	a paper robot can read existing papers and create new nodes or links in the knowledge graphs.
Approach:	They propose to automate the creation of new ideas by predicting links from the background KGs.
Outcome:	The proposed paper automates three tasks: read existing papers, create new ideas, predict links . the paper generated abstracts, conclusion and future work sections, and new titles are chosen over human-written ones up to 30%, 24% and 12% of the time.

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Xiao Long, Liansheng Zhuang, Li Aodi, Shafei Wang, Houqiang Li

Ensuring Readability and Data-fidelity using Head-modifier Templates in Deep Type Description Generation (P19-1)

Challenge:	Existing methods to embed entities and first-order logical queries in a vector space are often violated in real applications and limit their performance.
Approach:	They propose a Neural-based Mixture Probabilistic Query Embedding Model that embeds entities and first-order logical queries in a vector space.
Outcome:	The proposed model outperforms state-of-the-art methods on benchmark datasets.

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Jiangjie Chen, Ao Wang, Haiyun Jiang, Suo Feng, Chenguang Li, Yanghua Xiao

Challenge:	Existing generative methods overlook grammatical structure or make factual mistakes in generated texts.
Approach:	They propose a template-based method to ensure the readability of generated type descriptions . they also propose measurable metrics to measure the readibility of the generated type description .
Outcome:	The proposed method improves substantially compared with baselines and achieves state-of-the-art performance on both datasets.

Recurrent One-Hop Predictions for Reasoning over Knowledge Graphs (C18-1)

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Wenpeng Yin, Yadollah Yaghoobzadeh, Hinrich Schütze

Query2Particles: Knowledge Graph Reasoning with Particle Embeddings (2022.findings-naacl)

Challenge:	Large scale knowledge graphs (KGs) such as Freebase are generally incomplete.
Approach:	They propose a model that predicts entities at each step of mh-KB paths . the model is based on recurrent neural networks and vector representations of entities and relations .
Outcome:	The proposed models show state-of-the-art for two important multi-hop KG reasoning tasks.

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Jiaxin Bai, Zihao Wang, Hongming Zhang, Yangqiu Song

Graph Reasoning for Question Answering with Triplet Retrieval (2023.findings-acl)

Challenge:	Existing methods to answer complex logical queries on incomplete knowledge graphs with missing edges are needed to solve the problem.
Approach:	They propose a query embedding method that encodes queries and entities to the same embeddable space and then selects the answer entities based on similarities .
Outcome:	The proposed method can answer complex logical queries on incomplete knowledge graphs with missing edges.

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Shiyang Li, Yifan Gao, Haoming Jiang, Qingyu Yin, Zheng Li, Xifeng Yan, Chao Zhang, Bing Yin

Unifying Dual-Space Embedding for Entity Alignment via Contrastive Learning (2025.coling-main)

Challenge:	Existing methods to answer complex questions require reasoning over knowledge graphs (KGs) state-of-the-art methods constrain retrieved knowledge in local subgraphs and discard more diverse triplets that are disconnected but useful for question answering.
Approach:	They propose a method to retrieve the most relevant triplets from KGs and then rerank them, which are then concatenated with questions to be fed into language models.
Outcome:	The proposed method outperforms state-of-the-art methods on commonsenseQA and OpenbookQA datasets with 4.6% absolute accuracy.

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Cunda Wang, Weihua Wang, Qiuyu Liang, Feilong Bao, Guanglai Gao

Learning to Borrow– Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph Completion (2022.naacl-main)

Challenge:	Entity alignment (EA) aims to match identical entities across knowledge graphs (KGs) Graph neural network-based entity alignment methods have achieved promising results in Euclidean space, but KGs often contain complex local and hierarchical structures, which are hard to represent in a single space.
Approach:	They propose a method which unifies dual-space embedding to preserve the intrinsic structure of KGs.
Outcome:	The proposed method achieves state-of-the-art in structure-based EA on benchmark datasets.

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Huda Hakami, Mona Hakami, Angrosh Mandya, Danushka Bollegala

Reasoning with Trees: Faithful Question Answering over Knowledge Graph (2025.coling-main)

Challenge:	Existing methods to integrate text corpora with knowledge graphs (KGs) have been effective in various NLP tasks such as analyzing and predicting relationships between entities.
Approach:	They propose a method that borrows LDPs from entities that co-occur in sentences to represent entities that do not co-exist in a single sentence.
Outcome:	The proposed method improves the performance of prior methods such as TransE, DistMult, ComplEx and RotatE.

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Tiesunlong Shen, Jin Wang, Xuejie Zhang, Erik Cambria

One-Shot Relational Learning for Knowledge Graphs (D18-1)

Challenge:	Recent advances in large language models (LLMs) have shown remarkable progress in reasoning capabilities, yet they still face challenges in complex, multi-step reasoning tasks.
Approach:	They propose a framework that synergistically integrates LLMs with knowledge graphs (KGs) to enhance reasoning performance and interpretability.
Outcome:	The proposed framework outperforms existing state-of-the-art methods on two benchmark KGQA datasets and improves on the MCTS process.

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Wenhan Xiong, Mo Yu, Shiyu Chang, Xiaoxiao Guo, William Yang Wang

Taxonomy-Driven Knowledge Graph Construction for Domain-Specific Scientific Applications (2025.findings-acl)

Challenge:	Existing studies on knowledge graph completion require a large number of positive examples for each relation, but long-tail relations are more common in KGs and those newly added relations do not have many known triples for training.
Approach:	They propose a one-shot relational learning framework that utilizes the knowledge distilled by embedding models and learns a matching metric by considering both the learned embeddments and one-hop graph structures.
Outcome:	The proposed framework improves on existing embedding models and eliminates the need for retraining when dealing with newly added relations.

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Huitong Pan, Qi Zhang, Mustapha Adamu, Eduard Dragut, Longin Jan Latecki

Synergizing Multimodal Temporal Knowledge Graphs and Large Language Models for Social Relation Recognition (2025.emnlp-main)

Challenge:	Existing methods for constructing domain-specific knowledge graphs neglect curated taxonomies and LLMs fail to extract KGs in specialized domains.
Approach:	They propose a taxonomy-driven framework for constructing domain-specific knowledge graphs . they use structured taxonomies, Large Language Models and Retrieval-Augmented Generation .
Outcome:	The proposed framework can be adapted for other specialized domains.

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Haorui Wang, Zheng Wang, Yuxuan Zhang, Bo Wang, Bin Wu

From Alignment to Assignment: Frustratingly Simple Unsupervised Entity Alignment (2021.emnlp-main)

Challenge:	Recent advances in Large Language Models (LLMs) have limited capacity to model complex graph-structured relationships.
Approach:	They propose a low-coupling method synergizing multimodal temporal Knowledge Graphs and Large Language Models for social relation reasoning.
Outcome:	The proposed method exhibits state-of-the-art performance in social relation recognition . it bridges the gap between KGs and LLMs and will be released after acceptance .

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Xin Mao, Wenting Wang, Yuanbin Wu, Man Lan

KG-Adapter: Enabling Knowledge Graph Integration in Large Language Models through Parameter-Efficient Fine-Tuning (2024.findings-acl)

Challenge:	Existing methods for cross-lingual entity alignment rely on lexical matching and probability reasoning, but they inherit poor interpretability and low efficiency from neural networks.
Approach:	They propose a simple but effective unsupervised entity alignment method without neural networks that can be used to find the equivalent entities between crosslingual KGs.
Outcome:	Extensive experiments show that the proposed method beats advanced supervised methods across all datasets while having high efficiency, interpretability, and stability.

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Shiyu Tian, Yangyang Luo, Tianze Xu, Caixia Yuan, Huixing Jiang, Chen Wei, Xiaojie Wang

Enhancing Unsupervised Sentence Embeddings via Knowledge-Driven Data Augmentation and Gaussian-Decayed Contrastive Learning (2025.acl-long)

Challenge:	Large language models (LLMs) are criticized for lack of expertise and knowledge conflict . KG-Adapter is a parameter-level KG integration method for decoder-only LLMs .
Approach:	They propose a parameter-level KG integration method based on parameter-efficient fine-tuning . they use KG-Adapter to integrate knowledge graphs with LLMs and perform joint reasoning .
Outcome:	The proposed method outperforms the current state-of-the-art method on four datasets for two different tasks.

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Peichao Lai, Zhengfeng Zhang, Wentao Zhang, Fangcheng Fu, Bin Cui

Graph Language Models (2024.acl-long)

Challenge:	Existing methods for data augmentation neglect fine-grained knowledge, such as entities and quantities, leading to insufficient diversity and high data noise.
Approach:	They propose a pipeline-based data augmentation method via LLMs and introduce the Gaussian-decayed gradient-assisted Contrastive Sentence Embedding (GCSE) model to enhance unsupervised sentence embeddings.
Outcome:	The proposed method achieves state-of-the-art performance in semantic textual similarity tasks using fewer data samples and smaller LLMs.

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Moritz Plenz, Anette Frank

Query Structure Modeling for Inductive Logical Reasoning Over Knowledge Graphs (2023.acl-long)

Challenge:	Language Models (LMs) are the workhorses of NLP, but their interplay with structured knowledge graphs (KGs) is still actively researched.
Approach:	They propose a Graph Language Model (GLM) that integrates the strengths of both approaches and mitigates their weaknesses.
Outcome:	Empirical evaluations show that the proposed model surpasses both LM- and GNN-based baselines in supervised and zero-shot setting, demonstrating their versatility.

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Siyuan Wang, Zhongyu Wei, Meng Han, Zhihao Fan, Haijun Shan, Qi Zhang, Xuanjing Huang

KAPALM: Knowledge grAPh enhAnced Language Models for Fake News Detection (2023.findings-emnlp)

Challenge:	Existing methods for inductive reasoning over knowledge graphs lack the ability to model the logical structures of complex queries.
Approach:	They propose a structure-modeled textual encoding framework for inductive logical reasoning over KGs that encodes linearized query structures and entities using pre-trained language models to find answers.
Outcome:	The proposed framework encodes query structures and entities using pre-trained language models to find answers.

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Jing Ma, Chen Chen, Chunyan Hou, Xiaojie Yuan

Learning to Update Knowledge Graphs by Reading News (D19-1)

Challenge:	Existing methods of fake news detection focus on news entity information and ignore structured knowledge among news entities.
Approach:	They propose a model that fuses coarse- and fine-grained representations of entity knowledge from Knowledge Graphs (KGs) they identify entities in news content and link them to entities in KGs.
Outcome:	The proposed model outperforms state-of-the-art models on two benchmark datasets and is competitive in the few-shot scenario.

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Jizhi Tang, Yansong Feng, Dongyan Zhao

DIVINE: A Generative Adversarial Imitation Learning Framework for Knowledge Graph Reasoning (D19-1)

Challenge:	Existing methods to update knowledge graphs rely on elaborately designed IE systems and domain-specific rules.
Approach:	They propose a novel neural network method to update knowledge graphs (KGs) they use a text-based attention mechanism to guide updating messages through KGs .
Outcome:	The proposed method can effectively broadcast news information to KG structures and perform necessary link-adding or link-deleting operations to ensure the KG up-to-date according to news snippets.

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Ruiping Li, Xiang Cheng

ActiveEA: Active Learning for Neural Entity Alignment (2021.emnlp-main)

Challenge:	Existing knowledge graph reasoning methods require numerous trials for path-finding and require meticulous reward engineering to fit specific datasets.
Approach:	They propose a plug-and-play framework that uses generative adversarial imitation learning to enhance existing RL-based methods.
Outcome:	The proposed framework improves existing RL-based methods while eliminating reward engineering.

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Bing Liu, Harrisen Scells, Guido Zuccon, Wen Hua, Genghong Zhao

Enhancing Hyperbolic Knowledge Graph Embeddings via Lorentz Transformations (2024.findings-acl)

Challenge:	Existing approaches to combining knowledge Graphs (KGs) are incomplete but complementary to each other.
Approach:	They propose a novel Active Learning framework for neural EA that creates highly informative seed alignments to obtain more effective models with less annotation cost.
Outcome:	The proposed framework significantly improves sampling quality with good generality across different datasets, EA models and amount of bachelors.

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Xiran Fan, Minghua Xu, Huiyuan Chen, Yuzhong Chen, Mahashweta Das, Hao Yang

ClaimPKG: Enhancing Claim Verification via Pseudo-Subgraph Generation with Lightweight Specialized LLM (2025.findings-acl)

Challenge:	Existing methods for knowledge graph embedding rely on tangent approximation and are not fully hyperbolic.
Approach:	They propose a fully hyperbolic KGE method that represents entities as points in the Lorentz model and represents relations as the intrinsic transformation.
Outcome:	The proposed method captures various types of relations including hierarchical structures.

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Hoang Pham, Thanh-Do Nguyen, Khac-Hoai Nam Bui

Semi-supervised Entity Alignment via Joint Knowledge Embedding Model and Cross-graph Model (D19-1)

Challenge:	Existing verification methods rely on unstructured text corpora to break down claims . despite strong reasoning abilities, modern LLMs struggle with modular pipelines .
Approach:	They propose a framework that integrates knowledge graphs with LLM reasoning . they propose KGs provide structured, semantically rich representations .
Outcome:	The proposed framework outperforms baselines on the FactKG dataset by 9%-12% accuracy points across multiple categories.

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Chengjiang Li, Yixin Cao, Lei Hou, Jiaxin Shi, Juanzi Li, Tat-Seng Chua

NeuSTIP: A Neuro-Symbolic Model for Link and Time Prediction in Temporal Knowledge Graphs (2023.emnlp-main)

Challenge:	Entity alignment aims at integrating complementary knowledge graphs (KGs) from different sources or languages.
Approach:	They propose a semi-supervised entity alignment method by joint Knowledge Embedding model and Cross-Graph model to make better use of seed alignments to propagate over the entire graphs with KG-based constraints.
Outcome:	The proposed method can make better use of seed alignments to propagate over entire graphs with KG-based constraints.

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Ishaan Singh, Navdeep Kaur, Garima Gaur, null Mausam

Knowing the No-match: Entity Alignment with Dangling Cases (2021.acl-long)

Challenge:	Temporal Knowledge Graphs (KGs) are factual information repositories where a fact is associated with a time interval.
Approach:	They propose a temporal NS model for knowledge graph completion that performs link prediction and time interval prediction in a TKG.
Outcome:	The proposed model shows competitive performance on link prediction and time prediction.

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Zequn Sun, Muhao Chen, Wei Hu

Correcting on Graph: Faithful Semantic Parsing over Knowledge Graphs with Large Language Models (2025.findings-acl)

Challenge:	Existing approaches to find entities that cannot find alignment across knowledge graphs (KGs) despite their importance, knowledge graph is expensive and suffers from incompleteness.
Approach:	They propose a framework for entity alignment and dangling entity detection that can be used to abstain from predicting alignment for detected dangle entities.
Outcome:	The proposed framework can abstain from predicting alignment for detected dangling entities.

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Ruilin Zhao, Feng Zhao, Hong Zhang

Enhancing Contextual Word Representations Using Embedding of Neighboring Entities in Knowledge Graphs (2022.coling-1)

Challenge:	Complex multi-hop questions require comprehensive retrieval and reasoning.
Approach:	They propose a semantic parsing framework to establish faithful logical queries that connect LLMs and knowledge graphs.
Outcome:	The proposed framework outperforms state-of-the-art KGQA methods on knowledge-intensive questions.

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Ryoko Tokuhisa, Keisuke Kawano, Akihiro Nakamura, Satoshi Koide

Do Pre-trained Models Benefit Knowledge Graph Completion? A Reliable Evaluation and a Reasonable Approach (2022.findings-acl)

Challenge:	Existing methods for pre-trained language models lack explicit grounding in real-world entities.
Approach:	They propose a mechanism that integrates the structure of a KG into recent PLM architectures by generalizing the embeddings of neighboring entities.
Outcome:	The proposed method improves a classification task, entity typing task and language comprehension tasks.

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Xin Lv, Yankai Lin, Yixin Cao, Lei Hou, Juanzi Li, Zhiyuan Liu, Peng Li, Jie Zhou

Inductive Relation Prediction with Logical Reasoning Using Contrastive Representations (2022.emnlp-main)

Challenge:	Pre-trained language models capture factual knowledge from massive texts . but they are still quite behind the SOTA KGC models in terms of performance .
Approach:	They propose to use open-world assumption to evaluate PLM-based knowledge graph completion models . they propose to convert each triple and its support information into natural prompt sentences .
Outcome:	The proposed model is more accurate under the open-world assumption (OWA) this setting manual checks the correctness of knowledge that is not in KGs.

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Yudai Pan, Jun Liu, Lingling Zhang, Tianzhe Zhao, Qika Lin, Xin Hu, Qianying Wang

Breaking the Static Graph: Context-Aware Traversal for Graph-Based RAG (2026.findings-acl)

Challenge:	Existing methods for relation prediction in knowledge graphs (KGs) are limited by the inductive setting because entities in training process are finite.
Approach:	They propose a graph convolutional network-based model LogCo with logical reasoning by contrastive representations that extracts subgraphs and relational paths between two entities to supply the entity-independence.
Outcome:	The proposed model outperforms existing methods on twelve inductive datasets.

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Kwun Hang Lau, Fangyuan Zhang, Boyu Ruan, Yingli Zhou, Qintian Guo, Ruiyuan Zhang, Xiaofang Zhou

Multilingual Knowledge Graph Completion via Ensemble Knowledge Transfer (2020.findings-emnlp)

Challenge:	Recent advances in RAG focus on capturing multi-hop dependencies, but static Graphs fail to retrieve complete evidence chain.
Approach:	They propose a structure-aware approach to capture multi-hop dependencies using Knowledge Graphs and Personalized PageRank to capture semantic drift.
Outcome:	Experiments show that CatRAG outperforms state-of-the-art approaches . the proposed approach achieves substantial improvements in reasoning completeness .

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Xuelu Chen, Muhao Chen, Changjun Fan, Ankith Uppunda, Yizhou Sun, Carlo Zaniolo

Fact Verification on Knowledge Graph via Programmatic Graph Reasoning (2025.findings-emnlp)

Challenge:	Existing methods for learning missing facts in knowledge graphs are limited by insufficiency of alignment information and inconsistency of described facts.
Approach:	They propose a framework for embedding learning and ensemble knowledge transfer across KGs.
Outcome:	The proposed framework improves state-of-the-art methods on language-specific KGs.

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Yuanzhen Hao, Desheng Wu

Debate to Align: Reliable Entity Alignment through Two-Stage Multi-Agent Debate (2026.findings-acl)

Challenge:	Existing methods for fact verification on knowledge graphs use implicit reasoning to predict entailment between claims and KG triples.
Approach:	They propose a framework that integrates large language models for fact verification on knowledge graphs.
Outcome:	The proposed framework outperforms existing methods on knowledge graphs with 86.82% accuracy.

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Cunda Wang, Ziying Ma, Po Hu, Weihua Wang, Feilong Bao

Towards Automatic Bias Detection in Knowledge Graphs (2021.findings-emnlp)

Challenge:	Entity alignment (EA) aims to identify entities referring to the same real-world object across different knowledge graphs (KGs).
Approach:	They propose a reliable EA framework based on multi-agent debate that improves embedding quality and introduces a two-stage multi-role debate mechanism to enhance reliability.
Outcome:	The proposed framework improves embedding quality and the reasoning capability of LLMs while enabling more efficient debate-based reasoning.

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Daphna Keidar, Mian Zhong, Ce Zhang, Yash Raj Shrestha, Bibek Paudel

FRAG: A Flexible Modular Framework for Retrieval-Augmented Generation based on Knowledge Graphs (2025.findings-acl)

Challenge:	Recent studies have shown that knowledge graphs are prone to various social biases, and have proposed multiple methods for debiasing them.
Approach:	They propose a framework for identifying biases present in knowledge graph embeddings based on numerical bias metrics.
Outcome:	The proposed framework can be extended to further bias definitions and applications.

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Zengyi Gao, Yukun Cao, Hairu Wang, Ao Ke, Yuan Feng, S Kevin Zhou, Xike Xie

KERAG: Knowledge-Enhanced Retrieval-Augmented Generation for Advanced Question Answering (2025.findings-emnlp)

Challenge:	Existing methods for retrieval-augmented generation struggle with a trade-off between flexibility and retrieval quality.
Approach:	They propose a flexible modular KG-RAG framework that uses query text instead of KGs . they propose to use query text to infer the structural information of reasoning paths .
Outcome:	The proposed method achieves state-of-the-art performance with high efficiency and low resource consumption.

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Yushi Sun, Kai Sun, Yifan Ethan Xu, Xiao Yang, Xin Luna Dong, Nan Tang, Lei Chen

Adapting Meta Knowledge Graph Information for Multi-Hop Reasoning over Few-Shot Relations (D19-1)

Challenge:	Traditional Knowledge Graph Question Answering (KGQA) methods rely on semantic parsing to retrieve knowledge strictly necessary for answer generation.
Approach:	They propose a retrieval-filtering-summarization pipeline that enhances QA coverage by retrieving a broader subgraph likely to contain relevant information.
Outcome:	The proposed pipeline surpasses state-of-the-art solutions by about 7% in quality and exceeds GPT-4o (Tool) by 10-21%.

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Xin Lv, Yuxian Gu, Xu Han, Lei Hou, Juanzi Li, Zhiyuan Liu

Moritz Plenz, Juri Opitz, Philipp Heinisch, Philipp Cimiano, Anette Frank

Challenge:	Existing methods for multi-hop reasoning assume that every relation has enough triples for training . however, performance drops significantly on few-shot relations .
Approach:	They propose a meta-based multi-hop reasoning method that learns meta parameters from high-frequency relations that could quickly adapt to few-shot scenarios.
Outcome:	The proposed method outperforms state-of-the-art methods in few-shot scenarios on two public datasets from Freebase and NELL.

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Better Together: Enhancing Generative Knowledge Graph Completion with Language Models and Neighborhood Information (2023.findings-emnlp)

Challenge:	Arguments often do not make explicit how a conclusion follows from its premises . we present a method for constructing Contextualized Commonsense Knowledge Graphs (CCKGs) that is efficient and high-quality .
Approach:	They propose an unsupervised method for constructing Contextualized Commonsense Knowledge Graphs (CCKGs) they use triplet similarities to extract contextually relevant knowledge paths .
Outcome:	The proposed method outperforms baselines and a GPT-3 based system in a knowledge-intense argumentation task.

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Alla Chepurova, Aydar Bulatov, Yuri Kuratov, Mikhail Burtsev

Knowledge Is Flat: A Seq2Seq Generative Framework for Various Knowledge Graph Completion (2022.coling-1)

Challenge:	Knowledge graph completion (KGC) methods are computationally intensive and impractical for large-scale KGs.
Approach:	They propose to include node neighborhoods as additional information to improve KGC methods based on language models.
Outcome:	The proposed method outperforms KGT5 and conventional methods on inductive and transductive Wikidata subsets and shows its importance.

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Chen Chen, Yufei Wang, Bing Li, Kwok-Yan Lam

A Knowledge Graph Reasoning-Based Model for Computerized Adaptive Testing (2025.coling-main)

Challenge:	Knowledge Graph Completion (KGC) has been extended to multiple knowledge graph (KG) structures, initiating new research directions, e.g. static KGC, temporal KGC and few-shot KGC.
Approach:	They propose a generative framework that could tackle different verbalizable graph structures by unifying the representation of KG facts into "flat" text.
Outcome:	The proposed framework outperforms many competitive baselines and sets new state-of-the-art performance on five benchmarks.

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Xinyi Qiu, Zhiyun Chen

CRFR: Improving Conversational Recommender Systems via Flexible Fragments Reasoning on Knowledge Graphs (2021.emnlp-main)

Challenge:	Existing studies have failed to account for the differences in concept relevance when a question involves multiple concepts .
Approach:	They propose a Knowledge Graph Reasoning-Based Model for CAT that captures semantic and relational information between concepts and questions and incorporates multiple evaluation objectives.
Outcome:	The proposed model outperforms existing methods on three authentic educational datasets.

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Jinfeng Zhou, Bo Wang, Ruifang He, Yuexian Hou

Knowledge Graph Pooling and Unpooling for Concept Abstraction (2025.coling-main)

Challenge:	Existing conversational recommender systems (CRS) do not track the deep shift of user interest in conversations due to the complex of high-order and incomplete paths.
Approach:	They propose a conversational context-based reinforcement learning model which does explicit multi-hop reasoning on KGs with a contextual context-driven reinforcement learning framework.
Outcome:	Extensive experiments show that CRFR improves on paths of interest shift in knowledge graphs (KGs) .

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Juan Li, Wen Zhang, Zhiqiang Liu, Mingchen Tu, Mingyang Chen, Ningyu Zhang, Shijian Li

CoRI: Collective Relation Integration with Data Augmentation for Open Information Extraction (2021.acl-long)

Challenge:	Knowledge graph embedding (KGE) aims to embed entities and relations as vectors in a continuous space.
Approach:	They propose a framework with KG Pooling and unpooling and Contrastive Learning to abstract and encode latent concepts for better KG prediction.
Outcome:	The proposed framework outperforms baselines on link prediction task.

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Zhengbao Jiang, Jialong Han, Bunyamin Sisman, Xin Luna Dong

Information Extraction & Text Mining

Challenge:	Existing methods to integrate extracted knowledge from the Web to knowledge graphs (KGs) however, the predictions are made independently, which can be mutually inconsistent.
Approach:	They propose a relation integration model that aligns free-text relations to relations in a target KG . they propose combining two stages to make independent predictions and a collective model that accesses all candidate predictions.
Outcome:	The proposed model outperforms baseline models on two datasets and improves AUC from .677 to .748 and from 1.716 to 1.780.

Graph-Assisted Culturally Adaptable Idiomatic Translation for Indic languages (2025.findings-acl)

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Pratik Rakesh Singh, Kritarth Prasad, Mohammadi Zaki, Pankaj Wasnik

Multilinguality

Challenge:	a single source idiom can have multiple target-language equivalents depending on cultural references and contextual variations.
Approach:	They propose an adaptive graph neural network-based method that learns intricate mappings between idiomatic expressions and generalizes to both seen and unseen nodes during training.
Outcome:	The proposed method improves translation quality even in resource-constrained settings, facilitating improved idiomatic translation in smaller models.

Connecting the Dots: A Knowledgeable Path Generator for Commonsense Question Answering (2020.findings-emnlp)

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Peifeng Wang, Nanyun Peng, Filip Ilievski, Pedro Szekely, Xiang Ren

ARK: Answer-Centric Retriever Tuning via KG-augmented Curriculum Learning (2026.acl-long)

Challenge:	Existing QA systems do not have commonsense knowledge or cannot reason with it.
Approach:	They propose to augment a general commonsense QA framework with a knowledgeable path generator by extrapolating existing paths from a KG with 'state-of-the-art' language model.
Outcome:	The generated paths are interpretable, novel, and relevant to the task.

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Jiawei Zhou, Hang Ding, Haiyun Jiang

JointLK: Joint Reasoning with Language Models and Knowledge Graphs for Commonsense Question Answering (2022.naacl-main)

Challenge:	Retrieval-Augmented Generation (RAG) is a powerful framework for knowledge-intensive tasks, but its effectiveness in long-context scenarios is often bottlenecked by the retriever’s inability to distinguish sparse yet crucial evidence.
Approach:	They propose a framework that fine-tunes the retriever for Answer Alignment by identifying high-quality positive chunks by evaluating their sufficiency to generate the correct answer.
Outcome:	The proposed framework improves 14.5% over the base model and maintains strong efficiency for long-context RAG.

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Yueqing Sun, Qi Shi, Le Qi, Yu Zhang

Knowledge Graph-Enhanced Large Language Models via Path Selection (2024.findings-acl)

Challenge:	Existing KG-augmented models for commonsense question answering ignore the effectively fusing and reasoning over question context representations and the KG representations.
Approach:	They propose a novel model which combines a logical reasoning and a dynamic pruning mechanism to solve these limitations.
Outcome:	The proposed model improves existing models and performs interpretable reasoning on the CommonsenseQA and OpenBookQA datasets.

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Haochen Liu, Song Wang, Yaochen Zhu, Yushun Dong, Jundong Li

Right for Right Reasons: Large Language Models for Verifiable Commonsense Knowledge Graph Question Answering (2024.emnlp-main)

Challenge:	Large Language Models (LLMs) have shown unprecedented performance in various real-world applications, but they are known to generate factually inaccurate outputs.
Approach:	They propose a framework to integrate external knowledge extracted from Knowledge Graphs (KGs) they propose to generate scores for knowledge paths with input texts via latent semantic matching.
Outcome:	Experiments on real-world datasets validate the effectiveness of a framework to extract knowledge from Knowledge Graphs (KGs) incorporating external knowledge has become a promising strategy to improve the factual accuracy of LLM-generated outputs.

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Armin Toroghi, Willis Guo, Mohammad Mahdi Abdollah Pour, Scott Sanner

Solving Hard Analogy Questions with Relation Embedding Chains (2023.emnlp-main)

Challenge:	Existing Knowledge Graph Question Answering (KGQA) methods focus on answering factual questions, leaving questions involving commonsense reasoning unaddressed.
Approach:	They propose a commonsense KGQA methodology that axiomatically surfaces commonsensical knowledge of Large Language Models and grounding every factual reasoning step on KG triples.
Outcome:	The proposed method outperforms existing methods and reduces instances of hallucination and reasoning errors.

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Nitesh Kumar, Steven Schockaert

Graph Explorer: Training Faithful KG Agents with Visibility-Grounded Supervision (2026.findings-acl)

Challenge:	Existing approaches to model how concepts are related are incomplete and noisy.
Approach:	They propose to model relations as paths but associate their edges with relation embeddings.
Outcome:	The proposed representations are useful for solving hard analogy questions.

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Yifeng Chen, Sicheng Wan, Tianyi Zhang, Xuezhou Zhang

Wikontic: Constructing Wikidata-Aligned, Ontology-Aware Knowledge Graphs with Large Language Models (2026.eacl-long)

Challenge:	Large language models (LLMs) are strong reasoners but still hallucinate and make unreliable decisions on knowledge-intensive questions.
Approach:	They propose a pipeline that turns LLM into executable tool supervision without manual trace labeling.
Outcome:	The proposed model improves over a reproduced prompting baseline by +22.5/+16.2 points . it is based on a Graph Explorer pipeline that turns SPARQL into executable tool supervision without manual trace labeling.

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Alla Chepurova, Aydar Bulatov, Mikhail Burtsev, Yuri Kuratov

Enrich-on-Graph: Query-Graph Alignment for Complex Reasoning with LLM Enriching (2025.emnlp-main)

Challenge:	Knowledge graphs provide structured, verifiable grounding for large language models . current LLMs use KGs as auxiliary structures for text retrieval .
Approach:	They propose a pipeline that constructs KGs from open-domain texts using triplets and qualifiers.
Outcome:	The proposed pipeline outperforms existing methods in retrieval-augmented generation.

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Songze Li, Zhiqiang Liu, Zhengke Gui, Huajun Chen, Wen Zhang

HDiff: Confidence-Guided Denoising Diffusion for Robust Hyper-relational Link Prediction (2025.findings-emnlp)

Challenge:	Existing methods employ resource-intensive, non-scalable workflows reasoning on vanilla KGs, but overlook this gap.
Approach:	They propose a flexible framework that leverages LLMs’ prior knowledge to enrich KGs and bridge the semantic gap between queries and graphs.
Outcome:	The proposed framework bridges the semantic gap between structured knowledge graphs and unstructured queries while ensuring low computational costs, scalability, and adaptability across different methods.

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Xiangfeng Luo, Ruoxin Zheng, Jianqiang Huang, Hang Yu

CateEA: Enhancing Entity Alignment via Implicit Category Supervision (2025.coling-main)

Challenge:	Hyper-relational Knowledge Graph Completion (HKGC) is more sensitive to inherent noise, particularly struggling with two prevalent HKG-specific noise types: Intra-fact Inconsistency and Cross-fact Association Noise.
Approach:	They propose a conditional denoising diffusion framework that learns to reverse structured noise corruption.
Outcome:	The proposed framework outperforms state-of-the-art HKGC methods in a variety of noisy conditions.

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Guan Dong Feng, Tao Ren, Jun Hu, Dan dan Wang

Iterative Knowledge Graph Refinement and Integration for Medical Question Answering (2026.findings-acl)

Challenge:	Existing Entity Alignment methods neglect the inherent semantic information of entities, limiting alignment precision and robustness.
Approach:	They propose to combine implicit category information into multi-modal representations by generating pseudo-category labels from entity embeddings and integrating them into a multi-task learning framework.
Outcome:	Experiments on benchmark datasets show that CateEA outperforms state-of-the-art methods in various settings.

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Haochen Zou, Yongli Wang

Rule-Aware Reinforcement Learning for Knowledge Graph Reasoning (2021.findings-acl)

Challenge:	Existing graph-based RAG methods heuristically retrieve and refine question-relevant subgraphs, potentially introducing redundant and noisy factual information that is difficult for LLMs to process.
Approach:	They propose to integrate knowledge graphs (KGs) through retrieval-augmented generation methods to improve LLM reasoning by incorporating external trustworthy knowledge resources.
Outcome:	The proposed framework achieves state-of-the-art against baseline competitors on three medical QA benchmark datasets.

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Zhongni Hou, Xiaolong Jin, Zixuan Li, Long Bai

Leveraging 3D Gaussian for Temporal Knowledge Graph Embedding (2025.findings-emnlp)

Challenge:	Existing methods to reason missing facts on Knowledge Graphs face with serious incompleteness due to their black-box nature.
Approach:	They propose a multi-hop reasoning method that injects high quality symbolic rules into the model's reasoning process and employs partially random beam search.
Outcome:	The proposed method outperforms existing multi-hop reasoning methods in terms of Hit@1 and MRR.

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Jiang Li, Xiangdong Su, Guanglai Gao

Temporal Knowledge Graph Completion with Approximated Gaussian Process Embedding (2022.coling-1)

Challenge:	Representation learning in knowledge graphs (KGs) has focused on static data, yet many real-world knowledge graph are inherently dynamic.
Approach:	They propose a temporal embedding method inspired by 3D Gaussian Splatting where entities, relations, and timestamps are modeled as 3D gaussian distributions with learnable structured covariance.
Outcome:	The proposed method outperforms state-of-the-art methods on three benchmark TKG datasets.

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Linhai Zhang, Deyu Zhou

DET: A Dual-Encoding Transformer for Relational Graph Embedding (2024.lrec-main)

Challenge:	Existing TKGC methods are based on deterministic vector embeddings, which are not flexible and expressive enough.
Approach:	They propose a method that maps entities and relations to multivariate Gaussian processes by mapping global trends and local fluctuations in TKGs.
Outcome:	The proposed method can predict global trends and local fluctuations in the TKGs and can be optimized on two real-world benchmark datasets.

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Lingbing Guo, Zhuo Chen, Jiaoyan Chen, Qiang Zhang, Huajun Chen

Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs (D19-1)

Challenge:	Existing approaches to graph representation only consider the local neighbors, sacrificing the Transformer’s ability to attend to elements at any distance.
Approach:	They propose a dual-encoding Transformer architecture that uses a structural encoder and a semantic encoder to seek for semantically relevant nodes.
Outcome:	The proposed architecture achieves superior performance compared to state-of-the-art attention-based methods on complex relational graphs like KGs and citation networks.

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Mingyang Chen, Wen Zhang, Wei Zhang, Qiang Chen, Huajun Chen

XplainLLM: A Knowledge-Augmented Dataset for Reliable Grounded Explanations in LLMs (2024.emnlp-main)

Challenge:	Empirically, our model achieves state-of-the-art results on few-shot link prediction KG benchmarks.
Approach:	They propose a Meta Relational Learning framework to do few-shot link prediction in KGs by observing only a few associative triples.
Outcome:	The proposed model achieves state-of-the-art results on few-shot link prediction KG benchmarks.

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Zichen Chen, Jianda Chen, Ambuj Singh, Misha Sra

OpticE: A Coherence Theory-Based Model for Link Prediction (2022.coling-1)

Challenge:	Large Language Models (LLMs) have achieved remarkable success in natural language tasks, yet understanding their reasoning processes remains a significant challenge.
Approach:	They propose a dataset that includes 24204 instances where each instance interprets the LLM’s reasoning behavior using knowledge graphs and graph attention networks (GAT).
Outcome:	The proposed explanation framework reduces hallucinations and improves grounded explanation generation in large language models.

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Xiangyu Gui, Feng Zhao, Langjunqing Jin, Hai Jin

Linguistic Theories

Challenge:	Knowledge representation learning is a key step required for link prediction tasks with knowledge graphs (KGs).
Approach:	They propose a new embedding approach based on the physical phenomenon of optical interference to reduce the semantic ambiguity in KGs.
Outcome:	The proposed model can compete with existing methods on KG benchmarks.

FiDeLiS: Faithful Reasoning in Large Language Models for Knowledge Graph Question Answering (2025.findings-acl)

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Yuan Sui, Yufei He, Nian Liu, Xiaoxin He, Kun Wang, Bryan Hooi

How to Talk to Language Models: Serialization Strategies for Structured Entity Matching (2025.findings-naacl)

Challenge:	Existing retrieval-based or agent-based methods are prone to generating erroneous or hallucinated outputs.
Approach:	They propose a framework to leverage knowledge graphs as external knowledge sources to improve the factuality of LLM responses by anchoring answers to verifiable reasoning steps retrieved from KGs.
Outcome:	The proposed framework improves factuality and interpretability across benchmarks and reduces computational costs.

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Haoteng Yin, Jinha Kim, Prashant Mathur, Krishanu Sarker, Vidit Bansal

ODA: Observation-Driven Agent for integrating LLMs and Knowledge Graphs (2024.findings-acl)

Challenge:	Entity matching (EM) identifies whether two data records refer to the same entity . however, its performance heavily depends on how structured entities are “talked” through serialized text.
Approach:	They propose a novel serialization scheme for entities with complex relations in knowledge graphs based on random walks and use open-source LLMs to encode sampled semantic walks for matching.
Outcome:	The proposed scheme achieves leading performance on EM in canonical and heterogeneous KGs.

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Lei Sun, Zhengwei Tao, Youdi Li, Hiroshi Arakawa

Adaptive Graph Convolutional Network for Knowledge Graph Entity Alignment (2022.findings-emnlp)

Challenge:	Existing approaches to integrate large language models and knowledge graphs with LLMs often ignore the rich cognitive potential inherent in KGs.
Approach:	They propose an observation-driven agent framework that integrates KG reasoning abilities via global observation and integrates it into the action and reflection modules.
Outcome:	The proposed framework improves on several datasets and achieves 12.87% and 8.9% accuracy improvements.

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Renbo Zhu, Xukun Luo, Meng Ma, Ping Wang

Linking Surface Facts to Large-Scale Knowledge Graphs (2023.emnlp-main)

Challenge:	Entity alignment (EA) aims to identify equivalent entities from different Knowledge Graphs (KGs) noisy neighbors of entities transfer invalid information, drown out equivalent information, and ultimately reduce the performance of EA.
Approach:	They propose a method to deal with neighbor noises to reduce the performance of EA by capturing the differences and complementarities of multiple KGs.
Outcome:	The proposed framework outperforms the state-of-the-art methods in supervised and unsupervised settings.

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Gorjan Radevski, Kiril Gashteovski, Chia-Chien Hung, Carolin Lawrence, Goran Glavaš

Retrieval and Reasoning on KGs: Integrate Knowledge Graphs into Large Language Models for Complex Question Answering (2024.findings-emnlp)

Challenge:	Open Information Extraction (OIE) methods extract facts in the form of triples . ambiguity of these triples hinders their downstream usage .
Approach:	They propose a benchmark that measures fact linking performance on a granular triple slot level . they propose to use a system that can detect out-of-KG entities and predicates .
Outcome:	The proposed benchmark can measure fact linking performance on a granular triple slot level while also measuring if a system can recognize that a surface form has no match in the existing KG.

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Yixin Ji, Kaixin Wu, Juntao Li, Wei Chen, Mingjie Zhong, Xu Jia, Min Zhang

Does Pre-trained Language Model Actually Infer Unseen Links in Knowledge Graph Completion? (2024.naacl-long)

Challenge:	Large Language Models (LLMs) have performed impressively in various NLP tasks, but their inherent hallucination phenomena severely challenge their credibility in complex reasoning.
Approach:	They propose to integrate explainable Knowledge Graphs (KGs) with LLMs to alleviate hallucinations . they construct subgraphs to enhance the retrieval capabilities of KGs via CoT reasoning.
Outcome:	Extensive experiments on two KGQA datasets show that the proposed model achieves convincing performance compared to strong baselines.

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Yusuke Sakai, Hidetaka Kamigaito, Katsuhiko Hayashi, Taro Watanabe

Knowledge Graph-Guided Retrieval Augmented Generation (2025.naacl-long)

Challenge:	Knowledge Graph Completion (KGC) is a task that infers unseen relationships between entities . traditional embedding-based methods infer missing links using only training data . a pre-trained language model (PLM)-based KGC may be ineffective in practical applications .
Approach:	They propose to use knowledge Graph Completion (KGC) to infer unseen relationships . traditional embedding-based KGC methods infer missing links only from training data . they argue that pre-trained language models acquire inference abilities through pre-training .
Outcome:	The proposed method improves performance even though it does not use memorized knowledge.

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Xiangrong Zhu, Yuexiang Xie, Yi Liu, Yaliang Li, Wei Hu

Distill, Fuse, Pre-train: Towards Effective Event Causality Identification with Commonsense-Aware Pre-trained Model (2024.lrec-main)

Challenge:	Existing studies on RAG focus on semantic retrieval of isolated relevant chunks, which ignore their intrinsic relationships.
Approach:	They propose a framework that utilizes knowledge graphs to provide fact-level relationships between chunks, improving the diversity and coherence of the retrieved results.
Outcome:	Extensive experiments on the HotpotQA dataset and its variants demonstrate the advantages of KG2RAG compared to existing RAG-based approaches in terms of response quality and retrieval quality.

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Peixin Huang, Xiang Zhao, Minghao Hu, Zhen Tan, Weidong Xiao

Few-shot Low-resource Knowledge Graph Completion with Reinforced Task Generation (2023.findings-acl)

Challenge:	Existing methods to detect causal relationships in unstructured texts ignore trivial knowledge which may prejudice performance.
Approach:	They propose a pipeline to build a commonsense-aware pre-trained model which integrates reliable task-specific knowledge from commonsens graphs.
Outcome:	The proposed pipeline integrates reliable task-specific knowledge from commonsense graphs.

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Shichao Pei, Qiannan Zhang, Xiangliang Zhang

Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs (2024.findings-emnlp)

Challenge:	Existing few-shot learning-based models have difficulty alleviating the long-tail issue on low-resource KGs because of the lack of training tasks.
Approach:	They propose a few-shot low-resource knowledge graph completion framework that generates and selects beneficial few- shot tasks that complement current tasks.
Outcome:	The proposed framework is based on several real-world knowledge graphs and validates on multiple domains.

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Junjie Wang, Mingyang Chen, Binbin Hu, Dan Yang, Ziqi Liu, Yue Shen, Peng Wei, Zhiqiang Zhang, Jinjie Gu, Jun Zhou, Jeff Pan, Wen Zhang, Huajun Chen

Dynamic Anticipation and Completion for Multi-Hop Reasoning over Sparse Knowledge Graph (2020.emnlp-main)

Challenge:	Recent studies have attempted to enhance the performance of large language models (LLMs) in complex question-answering (QA) tasks by combining step-wise planning with external retrieval.
Approach:	They propose a framework for enhancing LLMs’ planning capabilities by using planning data derived from knowledge graphs (KGs).
Outcome:	The proposed framework improves LLMs’ planning capabilities by using knowledge graphs (KGs) the proposed framework is compared with existing frameworks on multiple datasets and shows that it is effective for large language models.

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Xin Lv, Xu Han, Lei Hou, Juanzi Li, Zhiyuan Liu, Wei Zhang, Yichi Zhang, Hao Kong, Suhui Wu

Knowledge Association with Hyperbolic Knowledge Graph Embeddings (2020.emnlp-main)

Challenge:	Existing reasoning methods for sparse KGs are incomplete and lack of evidential paths to target entities makes multi-hop reasoning difficult.
Approach:	They propose a multi-hop reasoning model over sparse KGs to solve this problem . they use latent prediction of embedding-based models to make the model perform more potential path search over sparses .
Outcome:	The proposed method outperforms state-of-the-art models on five datasets from Freebase, NELL and Wikidata.

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Zequn Sun, Muhao Chen, Wei Hu, Chengming Wang, Jian Dai, Wei Zhang

KG-Agent: An Efficient Autonomous Agent Framework for Complex Reasoning over Knowledge Graph (2025.acl-long)

Challenge:	Existing methods for knowledge graphs (KGs) depend on high embedding dimensions and hierarchical structures to achieve expressiveness.
Approach:	They propose a hyperbolic relational graph neural network for KG embedding and capture knowledge associations with a high-dimensional transformation.
Outcome:	Experiments on entity alignment and type inference show the proposed method is effective and efficient.

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Jinhao Jiang, Kun Zhou, Xin Zhao, Yang Song, Chen Zhu, Hengshu Zhu, Ji-Rong Wen

EffiQA: Efficient Question-Answering with Strategic Multi-Model Collaboration on Knowledge Graphs (2025.coling-main)

Challenge:	Existing methods to design the interaction strategy between large language models and knowledge graphs (KGs) are not effective for large language model (LLM)s to solve complex tasks due to the large volume and structured format of KG data.
Approach:	They propose an LLM-based agent framework that enables small LLMs to actively make decisions over knowledge graphs.
Outcome:	The proposed framework outperforms existing methods on in-domain and out-domain datasets using 10K samples.

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Zixuan Dong, Baoyun Peng, Yufei Wang, Jia Fu, Xiaodong Wang, Xin Zhou, Yongxue Shan, Kangchen Zhu, Weiguo Chen

ReKG-MCTS: Reinforcing LLM Reasoning on Knowledge Graphs via Training-Free Monte Carlo Tree Search (2025.findings-acl)

Challenge:	Existing approaches that integrate LLMs and KGs either underutilize the reasoning abilities of LLM or suffer from prohibitive computational costs due to tight coupling.
Approach:	They propose a framework that can strike a balance between performance and efficiency via an iterative paradigm.
Outcome:	The proposed framework can strike a balance between performance and efficiency via an iterative paradigm.

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Xiaozhuang Song, Shufei Zhang, Tianshu Yu

Multilingual Knowledge Graph Completion from Pretrained Language Models with Knowledge Constraints (2023.findings-acl)

Challenge:	Existing approaches to combining knowledge graphs with large language models face limitations in path exploration strategies or excessive computational overhead.
Approach:	They propose a training-free framework that synergizes Monte Carlo Tree Search with LLM capabilities to enable dynamic reasoning over KGs.
Outcome:	The proposed framework outperforms existing training-free methods and achieves competitive performance compared to fine-tuned baselines.

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Ran Song, Shizhu He, Shengxiang Gao, Li Cai, Kang Liu, Zhengtao Yu, Jun Zhao

Empowering Small-Scale Knowledge Graphs: A Strategy of Leveraging General-Purpose Knowledge Graphs for Enriched Embeddings (2024.lrec-main)

Challenge:	Existing methods for multilingual knowledge graph completion do not align with mKGC tasks because of their English-centric bias.
Approach:	They propose to use multilingual pretrained language models to solve queries in different languages by reasoning a tail entity.
Outcome:	The proposed method outperforms the previous SOTA on Hits@1 and Hits @10 by 12.32% and 16.03% on public datasets.

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Albert Sawczyn, Jakub Binkowski, Piotr Bielak, Tomasz Kajdanowicz

Exploring and Evaluating Attributes, Values, and Structures for Entity Alignment (2020.emnlp-main)

Challenge:	Existing approaches to augment LLMs with Knowledge Graphs (KGs) Knowledge-intensive tasks are prone to errors and require a large amount of knowledge to be understood.
Approach:	They propose a framework for augmenting LLMs through Knowledge Graphs (KGs) they propose KGs can be used to enhance performance in knowledge-intensive tasks .
Outcome:	Experimental results show that a small domain-specific KG can benefit from a performance boost in downstream tasks when linked to a substantial general-purpose KG.

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Zhiyuan Liu, Yixin Cao, Liangming Pan, Juanzi Li, Zhiyuan Liu, Tat-Seng Chua

SPOT: Zero-Shot Semantic Parsing Over Property Graphs (2025.findings-acl)

Challenge:	Entity alignment (EA) aims at building a Knowledge Graph (KG) of rich content by linking the equivalent entities from various KGs.
Approach:	They propose to use an attributed value encoder to partition a Knowledge Graph into subgraphs to model the various types of attribute triples efficiently.
Outcome:	The proposed method achieves significant improvements over 12 baselines in cross-lingual and monolingual datasets.

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Francesco Cazzaro, Justin Kleindienst, Sofia Márquez Gomez, Ariadna Quattoni

Conversational Semantic Parsing using Dynamic Context Graphs (2023.emnlp-main)

Challenge:	Knowledge Graphs (KGs) are becoming increasingly popular as a means of storing structured data.
Approach:	They propose a method to generate training data for semantic parsing over Property Graphs without human annotations by matching tree patterns to the KG and paraphrasing the query program with an LLM.
Outcome:	The proposed method generates training data for parsing over Property Graphs without human annotations on two property graph benchmarks utilizing the Cypher query language.

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Parag Jain, Mirella Lapata

Improving Time Sensitivity for Question Answering over Temporal Knowledge Graphs (2022.acl-long)

Challenge:	Existing work on conversational semantic parsing has focused on answering questions in isolation . whereas existing work on KBQA is focused on resolving questions in the context of natural language questions .
Approach:	They propose to model conversational semantic parsing over general purpose knowledge graphs with millions of entities and thousands of relation-types by exploiting its underlying structure and encoding it with a graph neural network.
Outcome:	The proposed model is better at processing discourse information and longer interactions . it is better than static models at handling ellipsis and coreference, the authors show .

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Chao Shang, Guangtao Wang, Peng Qi, Jing Huang

Can LLMs be Good Graph Judge for Knowledge Graph Construction? (2025.emnlp-main)

Challenge:	Temporal knowledge graphs record entity relations and when they occur in time . previous work fails to address time-related challenges such as time-order issues . paper proposes time-sensitive question answering framework to address these problems .
Approach:	They propose a time-sensitive question answering framework that uses temporal KGs to answer natural language questions.
Outcome:	The proposed framework outperforms the state-of-the-art on a new benchmark for question answering over temporal knowledge graphs.

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Haoyu Huang, Chong Chen, Zeang Sheng, Yang Li, Wentao Zhang

MindMap: Knowledge Graph Prompting Sparks Graph of Thoughts in Large Language Models (2024.acl-long)

Challenge:	Existing methods for converting unstructured text into structured Knowledge Graphs (KGs) have limitations such as large amount of noise, inaccurate knowledge, and hallucination .
Approach:	They propose a GraphJudge framework to reduce noise in real-world documents . they propose Graphjudge to fine-tune a LLM as a graph judge to enhance quality .
Outcome:	The proposed framework eliminates noise in real-world documents and improves the quality of generated KGs.

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Yilin Wen, Zifeng Wang, Jimeng Sun

Direct Fact Retrieval from Knowledge Graphs without Entity Linking (2023.acl-long)

Challenge:	Large language models suffer from limitations such as difficulty in incorporating new knowledge, generating hallucinations, and explaining their reasoning process.
Approach:	They propose a pipeline that leverages knowledge graphs to enhance LLMs’ inference and transparency by eliciting the mind map of LLM's, which reveals their reasoning pathways based on the ontology of knowledge.
Outcome:	The proposed pipeline enables LLMs to comprehend KG inputs and infer with a combination of implicit and external knowledge.

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Jinheon Baek, Alham Fikri Aji, Jens Lehmann, Sung Ju Hwang

From Alignment to Entailment: A Unified Textual Entailment Framework for Entity Alignment (2023.findings-acl)

Challenge:	Existing methods to retrieve facts from Knowledge Graphs (KGs) require additional labels and may accumulate errors .
Approach:	They propose a framework that directly retrieves facts from KGs given input text based on their representational similarities.
Outcome:	The proposed framework outperforms baselines on multiple fact retrieval tasks.

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Yu Zhao, Yike Wu, Xiangrui Cai, Ying Zhang, Haiwei Zhang, Xiaojie Yuan

An Unsupervised Joint System for Text Generation from Knowledge Graphs and Semantic Parsing (2020.emnlp-main)

Challenge:	Existing methods encode the triples of entities as embeddings and learn to align the embeddables, which prevents the direct interaction between the original information of the cross-KG entities.
Approach:	They propose to transform the triples into unified textual sequences and model the EA task as a bi-directional textual entailment task between the sequences of cross-KG entities.
Outcome:	The proposed approach outperforms the state-of-the-art methods on five cross-lingual datasets and allows the mutual enhancement of the heterogeneous information.

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Martin Schmitt, Sahand Sharifzadeh, Volker Tresp, Hinrich Schütze

ACENet: Attention Guided Commonsense Reasoning on Hybrid Knowledge Graph (2022.emnlp-main)

Challenge:	Knowledge graphs (KGs) vary greatly from one domain to another, resulting in a lack of domain-specific parallel graph-text data.
Approach:	They propose an unsupervised approach to graph-to-text generation and text-to graph knowledge extraction using WebNLG v2.1 and a new benchmark leveraging scene graphs from Visual Genome.
Outcome:	The proposed approach outperforms baselines on WebNLG v2.1 and a new benchmark leveraging scene graphs from Visual Genome.

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Chuzhan Hao, Minghui Xie, Peng Zhang

Croppable Knowledge Graph Embedding (2025.acl-long)

Challenge:	Existing approaches estimate plausibility of candidate choices separately based on their respective KGs, without considering the interference among different choices.
Approach:	They propose an Attention guided Commonsense rEasoning Network to integrate hybrid knowledge into the neural network.
Outcome:	The proposed model outperforms existing methods on CommonsenseQA and OpenbookQA datasets and shows significant performance gains.

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Yushan Zhu, Wen Zhang, Zhiqiang Liu, Mingyang Chen, Lei Liang, Huajun Chen

HyKGE: A Hypothesis Knowledge Graph Enhanced RAG Framework for Accurate and Reliable Medical LLMs Responses (2025.acl-long)

Challenge:	Knowledge Graph Embedding (KGE) is a common approach for Knowledge Grasse (KGs) in AI tasks.
Approach:	They propose a new KGE training framework MED that allows one training to obtain a croppable KGE model for multiple scenarios with different dimensional needs.
Outcome:	The proposed framework improves low-dimensional sub-models and makes high-dimensional models retain the low-dimension sub-modells’ capacity.

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Xinke Jiang, Ruizhe Zhang, Yongxin Xu, Rihong Qiu, Yue Fang, Zhiyuan Wang, Jinyi Tang, Hongxin Ding, Xu Chu, Junfeng Zhao, Yasha Wang

GreenKGC: A Lightweight Knowledge Graph Completion Method (2023.acl-long)

Challenge:	Recent approaches suffer from insufficient and repetitive knowledge retrieval, tedious and time-consuming query parsing, and monotonous knowledge utilization.
Approach:	They propose a retrieval-augmented generation framework which leverages LLMs’ powerful reasoning capacity to compensate for the incompleteness of user queries.
Outcome:	The proposed framework improves the accuracy and reliability of Large Language Models (LLMs) by combining the rich knowledge of LLMs with Hypothesis Outputs.

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Yun Cheng Wang, Xiou Ge, Bin Wang, C.-C. Jay Kuo

GLTW: Joint Improved Graph Transformer and LLM via Three-Word Language for Knowledge Graph Completion (2025.findings-acl)

Challenge:	Knowledge graph completion (KGC) aims to discover missing relationships in knowledge graphs (KGs).
Approach:	They propose a modularized knowledge graph completion solution that learns embeddings for entities and relations through a score function.
Outcome:	Experimental results show that GreenKGC outperforms SOTA methods in low dimensions and even better against high-dimensional models with a much smaller model size.

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Kangyang Luo, Yuzhuo Bai, Cheng Gao, Shuzheng Si, Zhu Liu, Yingli Shen, Zhitong Wang, Cunliang Kong, Wenhao Li, Yufei Huang, Ye Tian, Xuantang Xiong, Lei Han, Maosong Sun

Evidence-Focused Fact Summarization for Knowledge-Augmented Zero-Shot Question Answering (2024.emnlp-main)

Challenge:	Existing knowledge graphs lack the ability to integrate structural information into LLMs and output predictions deterministically.
Approach:	They propose a method which encodes structural information of KGs and merges it with LLMs to enhance KGC performance.
Outcome:	The proposed method improves the performance of KG Completion datasets on KGs by integrating structural information with LLMs.

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Sungho Ko, Hyunjin Cho, Hyungjoo Chae, Jinyoung Yeo, Dongha Lee

Message Passing for Hyper-Relational Knowledge Graphs (2020.emnlp-main)

Challenge:	Existing methods for enhancing QA performance of Large Language Models (LLMs) have limitations, including duplicated entities or relations, reduced evidence density, and failure to highlight crucial evidence.
Approach:	They propose an Evidence-focused Fact Summarization framework for enhanced QA with knowledge-augmented Large Language Models (LLMs) that incorporates external knowledge into LLMs to improve QA performance.
Outcome:	The proposed framework improves LLM’s zero-shot QA performance especially when noisy facts are retrieved.

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Mikhail Galkin, Priyansh Trivedi, Gaurav Maheshwari, Ricardo Usbeck, Jens Lehmann

Are Message Passing Neural Networks Really Helpful for Knowledge Graph Completion? (2023.acl-long)

Challenge:	Existing approaches to link prediction over knowledge graphs (KGs) are designed to work over triple-based models, where facts are represented as binary relations between entities.
Approach:	They propose a message passing based graph encoder - StarE capable of modeling hyper-relational knowledge graphs (KGs) they propose to encode an arbitrary number of additional information along with the main triple while keeping the semantic roles of qualifiers and triples intact.
Outcome:	The proposed model outperforms existing models across multiple benchmarks and shows that leveraging qualifiers is vital for link prediction.

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Juanhui Li, Harry Shomer, Jiayuan Ding, Yiqi Wang, Yao Ma, Neil Shah, Jiliang Tang, Dawei Yin

Generating Domain-Specific Knowledge Graphs from Large Language Models (2025.findings-acl)

Challenge:	Existing knowledge graphs are far from complete with large portions of triplets missing.
Approach:	They propose to use Graph Neural Networks to learn powerful embeddings to improve model performance.
Outcome:	The proposed models achieve comparable performance to MLP models, suggesting that MP may not be as crucial as previously thought.

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Marinela Parović, Ze Li, Jinhua Du

Should We Use a Fixed Embedding Size? Customized Dimension Sizes for Knowledge Graph Embedding (2025.coling-main)

Challenge:	Large language models (LLMs) have shown impressive world knowledge across different benchmarks and domains but their knowledge is inconveniently scattered across their billions of parameters.
Approach:	They propose a prompt-based method to extract knowledge solely from LLMs’ parameters to construct domain-specific KGs by a schema-based process.
Outcome:	The proposed method generates large domain-specific KGs containing tens of thousands of entities and relations, and then evaluates against Wikidata, an open-source human-created KG.

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Zhanpeng Guan, Zhao Zhang, Yiqing Wu, Fuwei Zhang, Yongjun Xu

Can Knowledge Graphs Make Large Language Models More Trustworthy? An Empirical Study Over Open-ended Question Answering (2025.acl-long)

Challenge:	Knowledge Graph Embedding (KGE) aims to project entities and relations into a low-dimensional space, which is crucial for knowledge completion, fusion, and inference.
Approach:	They propose to embed entities and relations into a low-dimensional space to enable knowledge Graphs to be effectively used by downstream AI tasks.
Outcome:	The proposed framework is universal and flexible, suitable for various KGE models.

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Yuan Sui, Yufei He, Zifeng Ding, Bryan Hooi

medIKAL: Integrating Knowledge Graphs as Assistants of LLMs for Enhanced Clinical Diagnosis on EMRs (2025.coling-main)

Challenge:	Existing benchmarks for integrating Knowledge Graphs with Large Language Models focus on closed-ended tasks, leaving a gap in evaluating performance on more complex, real-world scenarios.
Approach:	They propose a benchmark to evaluate LLMs augmented with KGs in open-ended, real-world question answering settings.
Outcome:	The proposed benchmark reflects practical complexities through diverse question types and incorporates metrics to quantify both hallucination rates and reasoning improvements in LLM+KG models.

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Mingyi Jia, Junwen Duan, Yan Song, Jianxin Wang

KG-GPT: A General Framework for Reasoning on Knowledge Graphs Using Large Language Models (2023.findings-emnlp)

Challenge:	Electronic Medical Records (EMRs) are the digitized record of a patient's medical and health information and are integral to modern healthcare.
Approach:	They propose a framework that combines Large Language Models (LLMs) with knowledge graphs (KGs) to enhance diagnostic capabilities.
Outcome:	The proposed framework assigns weighted importance to entities in medical records based on their type, enabling precise localization of candidate diseases within KGs.

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Jiho Kim, Yeonsu Kwon, Yohan Jo, Edward Choi

Few-shot Link Prediction on Hyper-relational Facts (2024.lrec-main)

Challenge:	Using large language models for complex reasoning tasks on knowledge graphs remains unexplored.
Approach:	They propose a multi-purpose framework leveraging large language models for complex reasoning tasks on knowledge graphs.
Outcome:	The proposed framework outperforms fully-supervised models in KG-based fact verification and KGQA benchmarks.

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Jiyao Wei, Saiping Guan, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng

BiQUE: Biquaternionic Embeddings of Knowledge Graphs (2021.emnlp-main)

Challenge:	Existing methods to predict missing elements in hyper-relational facts require high-quality data.
Approach:	They propose a task to predict a missing entity in a hyper-relational fact with limited support instances.
Outcome:	The proposed model outperforms existing models on three datasets.

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Jia Guo, Stanley Kok

Beyond Topology: Generative Node Importance Estimation via Structure-Guided Semantic Reasoning (2026.findings-acl)

Challenge:	Existing knowledge graph embeddings rely on geometric operations to model relational patterns such as symmetry and hierarchical semantics.
Approach:	They propose a new knowledge graph embedding model that integrates multiple geometric transformations to model multi-relational knowledge graphs.
Outcome:	Experiments on five datasets show that BiQUE can model symmetry, inversion, and composition.

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Kuofei Fang, Siyan Wu, Yu Guo, Bin Wu

Tree-of-Traversals: A Zero-Shot Reasoning Algorithm for Augmenting Black-box Language Models with Knowledge Graphs (2024.acl-long)

Challenge:	Existing methods for estimating node importance are limited and rely on topological aggregation.
Approach:	They propose a generative reasoning framework that leverages Large Language Models to generate precise importance scores for entities in Knowledge Graphs.
Outcome:	Extensive experiments show that the proposed framework outperforms existing methods and is generalized across domains.

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Elan Markowitz, Anil Ramakrishna, Jwala Dhamala, Ninareh Mehrabi, Charith Peris, Rahul Gupta, Kai-Wei Chang, Aram Galstyan

Self-supervised Quantized Representation for Seamlessly Integrating Knowledge Graphs with Large Language Models (2025.acl-long)

Challenge:	Knowledge graphs (KGs) complement Large Language Models (LLMs) by providing reliable, structured, domain-specific, and up-to-date external knowledge.
Approach:	They propose a zero-shot reasoning algorithm that augments black-box LLMs with one or more KGs.
Outcome:	The proposed algorithm significantly improves performance on question answering and KG question answering tasks.

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Qika Lin, Tianzhe Zhao, Kai He, Zhen Peng, Fangzhi Xu, Ling Huang, Jingying Ma, Mengling Feng

A Framework of Knowledge Graph-Enhanced Large Language Model Based on Question Decomposition and Atomic Retrieval (2024.findings-emnlp)

Challenge:	Large Language Models (LLMs) are gaining popularity due to their lack of knowledge hallucination and lack of a coherent model.
Approach:	They propose a self-supervised quantized representation method to compress KG structural and semantic knowledge into discrete codes that align the format of language sentences.
Outcome:	The proposed framework outperforms existing unsupervised methods producing more distinguishable codes on KG link prediction and triple classification tasks.

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Yading Li, Dandan Song, Changzhi Zhou, Yuhang Tian, Hao Wang, Ziyi Yang, Shuhao Zhang

Schema Generation for Large Knowledge Graphs Using Large Language Models (2025.findings-emnlp)

Challenge:	Existing methods to enhance LLMs with knowledge graphs have limited results . knowledge graph question answering (KGQA) provides interpretable reasoning for large language models .
Approach:	They propose a framework for KG-enhanced LLM based on question decomposition and atomic retrieval . they propose question decomposing tree as framework for LLM reasoning .
Outcome:	The proposed framework outperforms existing reasoning-based baselines on KGQA datasets.

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Bohui Zhang, Yuan He, Lydia Pintscher, Albert Meroño-Peñuela, Elena Simperl

CoG: Controllable Graph Reasoning via Relational Blueprints and Failure-Aware Refinement over Knowledge Graphs (2026.acl-long)

Challenge:	Schemas are a vital part of ontology engineering and require substantial knowledge engineers and domain experts to create them.
Approach:	They propose to use large language models to generate schemas in Shape Expressions (ShEx) to bridge the resource gap between knowledge engineers and domain experts.
Outcome:	The proposed pipelines use local and global information from knowledge graphs (KGs) to generate high-quality schemas in Shape Expressions (ShEx).

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Yuanxiang Liu, Songze Li, Xiaoke Guo, Zhaoyan Gong, Qifei Zhang, Huajun Chen, Wen Zhang

A Collaborative Reasoning Framework Powered by Reinforcement Learning and Large Language Models for Complex Questions Answering over Knowledge Graph (2025.coling-main)

Challenge:	Existing approaches to large language models often exhibit cognitive rigidity, causing reasoning stagnation.
Approach:	They propose a training-free framework that mimics the interplay between intuition and deliberation.
Outcome:	The proposed framework outperforms state-of-the-art approaches on three benchmarks.

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Zhiqiang Zhang, Wen Zhao

HydraRAG: Structured Cross-Source Enhanced Large Language Model Reasoning (2025.emnlp-main)

Challenge:	Knowledge Graph Question Answering (KGQA) aims to answer natural language questions by reasoning across multiple triples in knowledge graphs.
Approach:	They propose a collaborative reasoning framework powered by RL and LLMs to answer complex questions based on the knowledge graph.
Outcome:	The proposed model surpasses state-of-the-art models on four datasets.

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Xingyu Tan, Xiaoyang Wang, Qing Liu, Xiwei Xu, Xin Yuan, Liming Zhu, Wenjie Zhang

SLiNT: Structure-aware Language Model with Injection and Contrastive Training for Knowledge Graph Completion (2025.findings-emnlp)

Challenge:	Current RAG system retrieves evidence from knowledge graphs and text documents but has limitations in multi-hop reasoning, multi-entity questions, and source verification.
Approach:	They propose a training-free framework that unifies graph topology, document semantics, and source reliability to support deep, faithful reasoning in large language models.
Outcome:	The proposed framework outperforms the current hybrid model-based model-driven system by 20.3% and 30.1% on seven benchmark datasets.

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Mengxue Yang, Chun Yang, Jiaqi Zhu, Jiafan Li, Jingqi Zhang, Yuyang Li, Ying Li

Shrinking Embeddings for Hyper-Relational Knowledge Graphs (2023.acl-long)

Challenge:	Large language models (LLMs) lack structural information and semantic context to infer missing entities . large language models often lack structural signals to infuse missing entities into knowledge graphs .
Approach:	a modular framework integrates structural information and semantic context into a frozen LLM backbone for link prediction.
Outcome:	a new framework integrates KG-derived structural information and semantic context to infer missing entities.

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Bo Xiong, Mojtaba Nayyeri, Shirui Pan, Steffen Staab

SKRAG: A Retrieval-Augmented Generation Framework Guided by Reasoning Skeletons over Knowledge Graphs (2025.findings-emnlp)

Challenge:	Existing studies have focused on binary relational KGs where each fact is represented by a triple.
Approach:	They propose a geometric hyper-relational KG embedding method that explicitly models qualifier monotonicity, qualifier implication, and qualifier mutual exclusion.
Outcome:	The proposed method outperforms existing methods on three benchmarks of hyper-relational KGs.

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Xiaotong Xu, Yizhao Wang, Yunfei Liu, Shengyang Li

iQUEST: An Iterative Question-Guided Framework for Knowledge Base Question Answering (2025.acl-long)

Challenge:	Existing KG-based question answering frameworks face inefficient subgraph retrieval, limited reasoning capabilities, and high computational costs.
Approach:	They propose a Skeleton-guided RAG framework for knowledge graph question answering . SKRAG leverages a lightweight language model enhanced with the Finite State Machine constraint .
Outcome:	The proposed framework outperforms baselines and general-domain benchmarks on a KGQA dataset in the space science and utilization domain.

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Shuai Wang, Yinan Yu

Query2Triple: Unified Query Encoding for Answering Diverse Complex Queries over Knowledge Graphs (2023.findings-emnlp)

Challenge:	Large language models suffer from factual inaccuracies in knowledge-intensive domains.
Approach:	They propose a question-guided KBQA framework that iteratively decomposes complex queries into simpler sub-questions and integrates a Graph Neural Network (GNN) to look ahead and incorporate 2-hop neighbor information at each reasoning step.
Outcome:	The proposed framework improves on four benchmark datasets and four LLMs.

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Yao Xu, Shizhu He, Cunguang Wang, Li Cai, Kang Liu, Jun Zhao

Improving Knowledge Graph Completion with Structure-Aware Supervised Contrastive Learning (2024.emnlp-main)

Challenge:	Complex Query Answering (CQA) is a challenge task of Knowledge Graphs due to incompleteness of KGs.
Approach:	They propose a query embedding approach that decouples the training for simple and complex queries.
Outcome:	The proposed approach decouples training for simple and complex queries and achieves state-of-the-art performance over three public benchmarks.

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Jiashi Lin, Lifang Wang, Xinyu Lu, Zhongtian Hu, Wei Zhang, Wenxuan Lu

Complex Numerical Reasoning with Numerical Semantic Pre-training Framework (2025.emnlp-main)

Challenge:	Existing contrastive methods focus on individual triples, overlooking the broader structural connectivities and topologies of KGs.
Approach:	They propose a new contrastive learning framework that incorporates four tasks specifically tailored to KG data: Vertex-level CL, Neighbor-level Cl, Path-levelCL, and Relation composition level CL.
Outcome:	The proposed framework achieves SOTA performance under standard supervised and low-resource settings.

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Jun Zhang, Haihong E, Tianyi Hu, Yifan Zhu, Meina Song, Haoran Luo

Dynamic Heterogeneous-Graph Reasoning with Language Models and Knowledge Representation Learning for Commonsense Question Answering (2023.acl-long)

Challenge:	Numerical knowledge graphs (NKGs) are not limited to discrete entity-relation knowledge.
Approach:	They propose to combine numerical values and entities to solve multi-hop complex reasoning over incomplete knowledge graphs.
Outcome:	The proposed approach handles up to 102 types of complex numerical reasoning queries on three public datasets.

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Yujie Wang, Hu Zhang, Jiye Liang, Ru Li

R3-NL2GQL: A Model Coordination and Knowledge Graph Alignment Approach for NL2GQL (2024.findings-emnlp)

Challenge:	Existing methods for QA use knowledge graphs, but they ignore subgraph optimization and subgraph deepening.
Approach:	They propose a dynamic heterogeneous-graph reasoning method with LMs and knowledge representation learning that optimizes the structure and knowledge representing of the HKG using a two-stage pruning strategy and knowledge-representation learning.
Outcome:	The proposed method improves on existing methods at CommonsenseQA and OpenBookQA.

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Yuhang Zhou, Yu He, Siyu Tian, Yuchen Ni, Zhangyue Yin, Xiang Liu, Chuanjun Ji, Sen Liu, Xipeng Qiu, Guangnan Ye, Hongfeng Chai

Complex Logical Query Answering by Calibrating Knowledge Graph Completion Models (2024.findings-acl)

Challenge:	Adapting existing approaches for converting natural language to SQL encounters hurdles due to distinct nature of GQL compared to SQL.
Approach:	They propose a method that integrates both small and large Foundation Models for ranking, rewriting, and refining tasks.
Outcome:	The proposed approach integrates both small and large Foundation Models for ranking, rewriting, and refining tasks while capitalizing on the superior generalization and query generation prowess of larger models for the final transformation of natural language queries into GQL formats.

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Changyi Xiao, Yixin Cao

KG-CQR: Leveraging Structured Relation Representations in Knowledge Graphs for Contextual Query Retrieval (2025.emnlp-main)

Challenge:	Existing methods to solve complex logical queries are not well-calibrated . CKGC is lightweight and effective, allowing the model to quickly converge .
Approach:	They propose a method for calibrating KGC models to adapt to complex logical queries . they map the values of predictions of KGC to the range [0, 1] .
Outcome:	The proposed method can significantly boost model performance in complex logical query answering task.

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Chi Minh Bui, Ngoc Mai Thieu, Vinh Van Nguyen, Jason J. Jung, Khac-Hoai Nam Bui

LLM-Based Multi-Hop Question Answering with Knowledge Graph Integration in Evolving Environments (2024.findings-emnlp)

Challenge:	Existing methods that address corpus-level context loss focus on query enrichment through structured relation representations.
Approach:	They propose a framework for Contextual Query Retrieval that enriches queries with contextual representations derived from a corpus-centric KG.
Outcome:	The proposed framework outperforms strong baselines on RAGBench and MultiHop-RAG datasets in terms of retrieval effectiveness.

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Ruirui Chen, Weifeng Jiang, Chengwei Qin, Ishaan Rawal, Cheston Tan, Dongkyu Choi, Bo Xiong, Bo Ai

Learning Query Adaptive Anchor Representation for Inductive Relation Prediction (2023.findings-acl)

Challenge:	Existing methods for knowledge editing in Large Language Models face difficulties with multi-hop questions that require accurate fact identification and sequential logical reasoning.
Approach:	They propose a method that merges explicit knowledge representations of Knowledge Graphs with the linguistic flexibility of Large Language Models to convert free-form language into structured queries and fact triples.
Outcome:	The proposed method significantly surpasses state-of-the-art knowledge editing methods in the multi-hop question answering benchmark, MQuAKE.

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Zhiwen Xie, Yi Zhang, Jin Liu, Guangyou Zhou, Jimmy Huang

SAFE: Schema-Driven Approximate Distance Join for Efficient Knowledge Graph Querying (2025.emnlp-main)

Challenge:	Existing methods to infer the missing links between entities are limited to the transductive setting . Query Adaptive Anchor Representation (QAAR) model is based on entity-independent features .
Approach:	They propose a query adaptive anchor representation model which extracts one opening subgraph and performs reasoning by one time for all candidate triples.
Outcome:	The proposed model outperforms state-of-the-art models in relation prediction task.

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Sangoh Lee, Sungho Park, Wook-Shin Han

Systematic Assessment of Factual Knowledge in Large Language Models (2023.findings-emnlp)

Challenge:	Existing methods map an LLM-generated query graph onto the KG or let the LLM traverse the entire graph.
Approach:	They propose a framework that leverages schema graphs for robust query graph generation and efficient KG retrieval.
Outcome:	Extensive experiments on WebQSP, CWQ and GrailQA show that the proposed framework outperforms state-of-the-art methods in accuracy and efficiency.

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Linhao Luo, Trang Vu, Dinh Phung, Reza Haf

DrKGC: Dynamic Subgraph Retrieval-Augmented LLMs for Knowledge Graph Completion across General and Biomedical Domains (2025.findings-emnlp)

Challenge:	Existing question-answering benchmarks for large language models have limitations regarding factual knowledge coverage, as they focus on generic domains and overlap with pretraining data.
Approach:	They propose a framework to assess the factual knowledge of large language models by leveraging knowledge graphs.
Outcome:	The proposed framework generates questions and expected answers from the facts stored in a given knowledge graph and evaluates them with KGs in generic and specific domains.

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Yongkang Xiao, Sinian Zhang, Yi Dai, Huixue Zhou, Jue Hou, Jie Ding, Rui Zhang

FactKG: Fact Verification via Reasoning on Knowledge Graphs (2023.acl-long)

Challenge:	Knowledge graph completion (KGC) aims to predict missing triples in knowledge graphs . current approaches encode graph context in textual form, which fails to exploit its potential .
Approach:	a new method is proposed to predict missing triples in knowledge graphs by leveraging existing triples and textual information.
Outcome:	The proposed model learns structural embeddings and logical rules within the KG and extracts a subgraph for each query guided by the learned rules.

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Jiho Kim, Sungjin Park, Yeonsu Kwon, Yohan Jo, James Thorne, Edward Choi

Relation-Aware Question Answering for Heterogeneous Knowledge Graphs (2023.findings-emnlp)

Challenge:	knowledge graphs (KGs) have not been fully utilized as a knowledge source for fact verification.
Approach:	They propose a dataset to enable the community to better use knowledge graphs . they propose 108k natural language claims with five types of reasoning .
Outcome:	The proposed dataset consists of 108k natural language claims with five types of reasoning . authors believe the proposed method can advance reliability and practicality .

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Haowei Du, Quzhe Huang, Chen Li, Chen Zhang, Yang Li, Dongyan Zhao

Tree-KG: An Expandable Knowledge Graph Construction Framework for Knowledge-intensive Domains (2025.acl-long)

Challenge:	Existing retrieval-based approaches to solve multihop Knowledge Base Question Answering (KBQA) fail to utilize information from head-tail entities and the semantic connection between relations to enhance the information capturing of relations in KGs.
Approach:	They propose to use a dual relation graph to find the answer entity in a knowledge graph . they use primal entity graph reasoning, dual relation grafitment and interaction .
Outcome:	The proposed approach achieves significant performance gain over the prior state-of-the-art on two public datasets, WebQSP and CWQ.

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Songjie Niu, Kaisen Yang, Rui Zhao, Yichao Liu, Zonglin Li, Hongning Wang, Wenguang Chen

Less is More: Making Smaller Language Models Competent Subgraph Retrievers for Multi-hop KGQA (2024.findings-emnlp)

Challenge:	Knowledge graphs are a useful tool for organizing complex data in knowledge-intensive domains.
Approach:	They propose an expandable framework that combines structured domain texts with advanced semantic techniques to create a tree-like graph from textbooks.
Outcome:	The proposed framework surpasses competing methods in the text-Annotated dataset with high scores on the Text-Annalytated data.

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Wenyu Huang, Guancheng Zhou, Hongru Wang, Pavlos Vougiouklis, Mirella Lapata, Jeff Pan

Context-Aware Adapter Tuning for Few-Shot Relation Learning in Knowledge Graphs (2024.emnlp-main)

Challenge:	Recent studies suggest that Knowledge Graphs (KGs) contain valuable external knowledge for LLMs.
Approach:	They propose to model a conditional subgraph retrieval task handled by small language models and use a subgraph identifier as a special token to retrieve subgraphs.
Outcome:	The proposed model achieves competitive retrieval performance compared to state-of-the-art models relying on 7B parameters.

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Liu Ran, Zhongzhou Liu, Xiaoli Li, Yuan Fang

Generate-on-Graph: Treat LLM as both Agent and KG for Incomplete Knowledge Graph Question Answering (2024.emnlp-main)

Challenge:	Existing methods to predict instances for missing relations on knowledge graphs are limited by their limited training examples.
Approach:	They propose a context-aware adapter for few-shot relation learning in KGs . they propose tunable relation adaptation and contextual information for each relation .
Outcome:	Experiments on three benchmark KGs validate the superiority of RelAdapter over state-of-the-art methods.

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Yao Xu, Shizhu He, Jiabei Chen, Zihao Wang, Yangqiu Song, Hanghang Tong, Guang Liu, Jun Zhao, Kang Liu

Interactive Semantic Parsing with Reinforcement Learning for Knowledge Graph Reasoning (2026.findings-acl)

Challenge:	Existing methods to integrate LLMs with Knowledge Graphs (KGs) however, these methods are often incomplete to cover all the knowledge required to answer questions.
Approach:	They propose to integrate LLMs with Knowledge Graphs (KGs) to address insufficient knowledge and hallucination issues in Large Language Models.
Outcome:	The proposed method outperforms existing methods on two datasets.

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Yurun Song, Xiangqing Shen, Jianfei Yu, Rui Xia

Multi-perspective Improvement of Knowledge Graph Completion with Large Language Models (2024.lrec-main)

Challenge:	Existing approaches to improve LLM reliability rely on factual hallucinations . Existing methods rely only on graph traversal, resulting in imprecise retrieval and heavy post-processing burdens.
Approach:	They propose a framework that integrates knowledge Graphs as structured, high-fidelity buffers to enhance LLM reliability.
Outcome:	The proposed framework allows logical constraints to be dynamically interleaved with graph search while optimizing via reinforcement learning with only final answer feedback eliminates the need for gold program annotations.

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Derong Xu, Ziheng Zhang, Zhenxi Lin, Xian Wu, Zhihong Zhu, Tong Xu, Xiangyu Zhao, Yefeng Zheng, Enhong Chen

Towards Faithful Knowledge Graph Explanation Through Deep Alignment in Commonsense Question Answering (2024.emnlp-main)

Challenge:	Knowledge graph completion (KGC) is a widely used method to tackle incompleteness in knowledge graphs (KGs).
Approach:	They propose a general framework to compensate for the deficiency of contextualized knowledge by querying large language models from various perspectives.
Outcome:	The proposed framework improves knowledge graph completion (KGC) by querying large language models from various perspectives.

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Weihe Zhai, Arkaitz Zubiaga, Bingquan Liu, Chengjie Sun, Yalong Zhao

AutoGraph-R1: End-to-End Reinforcement Learning for Knowledge Graph Construction (2026.acl-long)

Challenge:	Current methods for generating faithful explanations overlook path decoding faithfulness, leading to divergence between graph encoder outputs and model predictions.
Approach:	They propose an algorithm to assess KG representation reliability and an LM-KG distribution-aware Alignment algorithm to improve explanation faithfulness without ground truth.
Outcome:	The proposed algorithm improves explanation faithfulness without ground truth and significantly improves fidelity and model performance.

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Hong Ting Tsang, Jiaxin Bai, Haoyu Huang, Qiao Xiao, Tianshi Zheng, Baixuan Xu, Shujie Liu, Yangqiu Song

EasyEA: Large Language Model is All You Need in Entity Alignment Between Knowledge Graphs (2025.findings-acl)

Challenge:	Currently, knowledge graphs are decoupled from their downstream application, resulting in suboptimal graph structures.
Approach:	They propose a framework to directly optimize KG construction for task performance using Reinforcement Learning (RL).
Outcome:	The proposed framework improves performance across multiple QA benchmarks and consistently achieves significant performance gains over task-agnostic baseline graphs.

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Jingwei Cheng, Chenglong Lu, Linyan Yang, Guoqing Chen, Fu Zhang

Prompt-fused Framework for Inductive Logical Query Answering (2024.lrec-main)

Challenge:	Entity alignment (EA) aims to identify entities in different knowledge graphs (KGs) that represent the same real-world object.
Approach:	They propose an end-to-end EA framework based on large language models that requires no training to implement.
Outcome:	The proposed framework significantly reduces the reliance on seed entity pairs while achieving state-of-the-art (SOTA) performance on diverse datasets.

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Zezhong Xu, Wen Zhang, Peng Ye, Lei Liang, Huajun Chen

Just KIDDIN’ : Knowledge Infusion and Distillation for Detection of INdecent Memes (2025.findings-acl)

Challenge:	Existing methods for addressing logical queries on knowledge graphs neglect missing edges in KGs . Existing approaches focus on addressing missing edges, thereby neglecting the emergence of new entities .
Approach:	They propose a query-aware prompt-fused framework that addresses embedding of emerging entities . they propose to use a symbolic query to gather information relevant to the query .
Outcome:	The proposed framework addresses embedding of emerging entities through contextual information aggregation.

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Rahul Garg, Trilok Padhi, Hemang Jain, Ugur Kursuncu, Ponnurangam Kumaraguru

Decoding on Graphs: Faithful and Sound Reasoning on Knowledge Graphs through Generation of Well-Formed Chains (2025.acl-long)

Challenge:	Existing approaches to detect toxicity in online multimodal environments require common-sense reasoning and contextual awareness.
Approach:	They propose a hybrid neurosymbolic framework that unifies distillation of implicit contextual knowledge from Large Vision-Language Models and infusion of explicit relational semantics through sub-graphs from Knowledge Graphs.
Outcome:	The proposed framework outperforms state-of-the-art models on two datasets with improvements of 0.5%, and 10.6% in HatefulMemes Benchmark.

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Kun Li, Tianhua Zhang, Xixin Wu, Hongyin Luo, James R. Glass, Helen M. Meng

Large Language Models Meet Knowledge Graphs for Question Answering: Synthesis and Opportunities (2025.emnlp-main)

Challenge:	Existing research on the utilization of Knowledge Graphs (KGs) for large language models (LLMs) relies on subgraph retriever or iterative prompting, overlooking the potential synergy of LLMs’ step-wise reasoning capabilities and KGs’ structural nature.
Approach:	They propose a graph-aware constrained decoding framework that facilitates a deep synergy between LLMs and KGs by constraint derived from the topology of the KG.
Outcome:	The proposed framework can provide faithful and sound reasoning for KGQA.

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Chuangtao Ma, Yongrui Chen, Tianxing Wu, Arijit Khan, Haofen Wang

KARPA: A Training-free Method of Adapting Knowledge Graph as References for Large Language Model’s Reasoning Path Aggregation (2025.findings-acl)

Challenge:	Large language models (LLMs) have shown remarkable performance on question-answering tasks due to their superior capabilities in natural language understanding and generation.
Approach:	They propose a structured taxonomy that categorizes the methodology of synthesizing LLMs and knowledge graphs for QA according to the categories of QA and the KG’s role when integrating with LLM.
Outcome:	The proposed taxonomy categorizes the methods according to the categories of QA and the KG’s role when integrating with LLMs.

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Siyuan Fang, Kaijing Ma, Tianyu Zheng, Xeron Du, Ningxuan Lu, Ge Zhang, Qingkun Tang

HyperKGR: Knowledge Graph Reasoning in Hyperbolic Space with Graph Neural Network Encoding Symbolic Path (2025.emnlp-main)

Challenge:	Existing methods for large language models (LLMs) are limited by step-by-step decision-making on KGs, or require fine-tuning or pre-training on specific KG.
Approach:	They propose a framework that harnesses the global planning abilities of large language models (LLMs) for efficient and accurate KG reasoning.
Outcome:	Extensive experiments show that the proposed framework achieves state-of-the-art performance in KGQA tasks, delivering both high efficiency and accuracy.

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Lihui Liu

LLMs as Knowledge Graph Refiners: Mitigating Factual Inconsistencies in Generative Knowledge Extraction (2026.acl-long)

Challenge:	Existing methods for linking knowledge graphs are incomplete and rely on Euclidean embeddings . a hyperbolic GNN framework embeds recursive learning trees in hyperbolical space .
Approach:	They propose a hyperbolic GNN framework that embeds recursive learning trees in hyperbolical space and generates query-specific embeddings.
Outcome:	The proposed framework outperforms state-of-the-art methods on multiple benchmark datasets.

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Donghyun Kim, Hyeongjun Yang, Seokju Hwang, Kyong-Ho Lee, Chanhee Lee

Enhancing Complex Reasoning in Knowledge Graph Question Answering through Query Graph Approximation (2025.findings-acl)

Challenge:	Knowledge graphs (KGs) represent real-world entities and their relations in a structured form.
Approach:	They propose a framework that performs triple-level refinement on KGs constructed via GKE.
Outcome:	The proposed framework improves KG quality from diverse perspectives.

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Hongjun Jeong, Minji Kim, Heesoo Jung, Ko Keun Kim, Hogun Park

EA-Agent: A Structured Multi-Step Reasoning Agent for Entity Alignment (2026.acl-long)

Challenge:	Existing knowledge-grounded question answering frameworks lack essential triplets related to the questions . Existing approaches to knowledge-based QA are incomplete in the context of KGs .
Approach:	They propose a framework to provide answers to structured queries by leveraging Knowledge Graphs.
Outcome:	The proposed framework outperforms existing methods on QA tasks where KGs are incomplete . the framework is based on a set of data from a dataset of QA questions .

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Yixuan Nan, Xixun Lin, Yanmin Shang, Ge Zhang, Zheng Fang, Fang Fang, Yanan Cao

Explainability & Interpretability in NLP

Challenge:	Entity alignment (EA) aims to identify entities across different knowledge graphs (KGs) that refer to the same real-world object.
Approach:	They propose to use large language models to integrate semantic knowledge into EA to identify entities across different knowledge graphs that refer to the same object.
Outcome:	The proposed agent outperforms existing methods and achieves state-of-the-art performance on three benchmark datasets.

G-HiRel: Enhancing the Adaption to Knowledge Updating for Large Language Model Reasoning (2026.findings-acl)

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Yudai Pan, Jiajie Hong, Tianzhe Zhao, Lingyun Song, Lingling Zhang, Yixin Chen, Xuequn Shang

Meta-Semantics Augmented Few-Shot Relational Learning (2025.emnlp-main)

Challenge:	Large language models (LLMs) have good performance in multiple reasoning tasks, but are limited to adapt the rapid knowledge updates in the real-world scenario.
Approach:	They propose an LLM reasoning framework with hierarchical relational retrieval for large-scale knowledge updating, named G-HiRel.
Outcome:	The proposed framework achieves superiority in terms of accuracy and interpretability on three benchmarks.

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Han Wu, Jie Yin

Thought-Action Graph Reasoning: Faithful and Efficient Reasoning of Large Language Models via Reusing Past Experience (2026.findings-acl)

Challenge:	Existing methods for few-shot relational learning on knowledge graphs focus on leveraging specific relational information, but rich semantics inherent in KGs have been overlooked.
Approach:	They propose a meta-learning framework that integrates meta-semantics with relational information for few-shot relational learning.
Outcome:	Extensive experiments on two real-world KG benchmarks validate the effectiveness of PromptMeta in adapting to new relations with limited supervision.

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Zhixiao Qi, Feng Huang, Yunqi Zhang, Shijie Zhang, Qingqing Sun, Yongfeng Huang, Minghu Jiang, Shuai Chen, Tianyi Zhang

EvoMemKG: An Evolvable Memory Agent for Multi-hop Knowledge Graph Reasoning (2026.findings-acl)

Challenge:	Existing methods for integrating knowledge graphs with LLMs suffer from poor generalization or low reasoning efficiency.
Approach:	They propose a thought-action Graph (TAG) that decomposes LLM-KG interaction trajectories into fine-grained semantic operators and guides LLM to execute on them.
Outcome:	The proposed paradigm outperforms state-of-the-art methods on KGQA benchmarks while reducing the number of LLM calls and generated tokens.

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Shiyu Tian, Shuyue Xing, Zhuoxin Han, Caixia Yuan, Xiaojie Wang

SocraticKG: Knowledge Graph Construction via QA-Driven Fact Extraction (2026.findings-acl)

Challenge:	Existing methods for integrating knowledge graphs with large language models lack continuous learning capabilities.
Approach:	They propose an agent framework with a dynamic, evolvable memory mechanism specifically designed for KG reasoning.
Outcome:	EvoMemKG achieves state-of-the-art performance without training or tools . it achieves improvements of up to 20% over baseline on multi-hop queries .

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Sanghyeok Choi, Woosang Jeon, Kyuseok Yang, Taehyeong Kim

LogosKG: Hardware-Optimized Scalable and Interpretable Knowledge Graph Retrieval (2026.acl-long)

Challenge:	Existing approaches to construct knowledge graphs struggle with factual coverage and information loss.
Approach:	They propose an automated KG construction method that introduces question-answer pairs as a structured intermediate representation to unfold document-level semantics prior to triple extraction.
Outcome:	The proposed method achieves superior factual retention while maintaining high structural cohesion even as extracted knowledge volume substantially expands.

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He Cheng, Yifu Wu, Saksham Khatwani, Maya Kruse, Dmitriy Dligach, Timothy A. Miller, Majid Afshar, Yanjun Gao

MegaRAG: Multimodal Knowledge Graph-Based Retrieval Augmented Generation (2026.acl-long)

Challenge:	Existing systems struggle to balance efficiency, scalability, and interpretability.
Approach:	They propose a hardware-aligned framework that enables scalable and interpretable k-hop retrieval on large KGs.
Outcome:	The proposed framework scales to billion-edge graphs without loss of retrieval fidelity.

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Chi-Hsiang Hsiao, Yi-Cheng Wang, Tzung-Sheng Lin, Yi-Ren Yeh, Chu-song Chen