Papers with KG
Efficient Entity Embedding Construction from Type Knowledge for BERT (2022.findings-aacl)
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| Challenge: | Existing work has shown advantages of incorporating knowledge graphs (KGs) into BERT for various NLP tasks. |
| Approach: | They propose to integrate knowledge graphs into BERT to train entity embeddings to include rich information of factual knowledge. |
| Outcome: | The proposed models perform very well when combined with context. |
Stage-wise Fine-tuning for Graph-to-Text Generation (2021.acl-srw)
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| Challenge: | Graph-to-text generation has benefited from pre-trained language models (PLMs) but they fail to fully utilize the structure information of the input graph. |
| Approach: | They propose a structured graph-to-text model with a two-step fine-tuning mechanism which first fine-tracks model on Wikipedia before adapting to graph- to-text generation. |
| Outcome: | The proposed model improves the performance of the English WebNLG 2017 dataset by using tree-level embeddings to capture the inter-dependency structures of the input graph. |
Improving Conversational Recommendation Systems’ Quality with Context-Aware Item Meta-Information (2022.findings-naacl)
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| Challenge: | Existing approaches to integrate the recommendation function and dialog generation function smoothly are lacking. |
| Approach: | They propose to integrate dialog context for recommendation and dialog generation better using a pre-trained language model and an item metadata encoder to integrate the recommendation and dialogue generation. |
| Outcome: | The proposed architecture improves the integration of recommendation and dialog generation functions. |
KE-GCL: Knowledge Enhanced Graph Contrastive Learning for Commonsense Question Answering (2022.findings-emnlp)
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| Challenge: | Existing models for commonsense question answering lack effective representations of knowledge graphs. |
| Approach: | They propose a Knowledge Enhanced Graph Contrastive Learning model by incorporating contextual descriptions into QA pairs and adopting a graph contrastive learning scheme. |
| Outcome: | The proposed model outperforms existing methods consistently on two benchmark datasets. |
KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation (2021.tacl-1)
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| 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. |
Pingan Smart Health and SJTU at COIN - Shared Task: utilizing Pre-trained Language Models and Common-sense Knowledge in Machine Reading Tasks (D19-60)
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| Challenge: | Existing approaches to represent knowledge in the low-dimensional space are to leverage large-scale unsupervised text corpus to train fixed or contextual representations. |
| Approach: | They propose to leverage large-scale unsupervised text corpus to train fixed or contextual language representations and to express knowledge into a knowledge graph (KG) they incorporate distributional representations of a KG onto the representations from pre-trained language models, via simply concatenation or multi-head attention. |
| Outcome: | The proposed models outperform the other models on the COIN: COmmonsense INference in Natural Language Processing (COIN) Workshop datasets. |
DCMKC: A Dual Consistency Matching Approach for Multi-hop Question Answering in LLMs (2025.findings-emnlp)
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| Challenge: | Existing reasoning based on chains of thought (CoTs) fails to find logical connections between reasoning steps . |
| Approach: | They propose a method to match KG reasoning chains with CoTs based on semantic similarity . they use a knowledge graph to find relevant information "within" each reasoning step . |
| Outcome: | The proposed method outperforms baselines on multi-answer questions with 5.1% improvement over baselines. |
PerKGQA: Question Answering over Personalized Knowledge Graphs (2022.findings-naacl)
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| Challenge: | Existing methods for question answering over knowledge graphs have focused on generalizable or generic knowledge, which assumes there is a predefined global KG for all queries. |
| Approach: | They propose to use a non-parametric technique that employs case-based reasoning and a parametric approach using graph neural networks to query a predefined knowledge graph (KG) |
| Outcome: | The proposed methods outperform strong baselines on an academic and an internal dataset by 6.5% and 10.5%. |
DynaSemble: Dynamic Ensembling of Textual and Structure-Based Models for Knowledge Graph Completion (2024.acl-short)
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| Challenge: | Existing approaches to Knowledge Graph Completion use textual descriptions of the KG entities and relations to perform the task. |
| Approach: | They propose a method to combine two popular approaches to Knowledge Graph Completion . structure-based models perform better when gold answer is easily reachable . textual models exploit textual descriptions to give good performance . |
| Outcome: | The proposed method achieves 6.8 pt MRR and 8.3 pTits@1 gains over the best baseline model for WN18RR dataset. |
Relation Embedding with Dihedral Group in Knowledge Graph (P19-1)
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| Challenge: | Existing models for link prediction fail to capture relation compositions, resulting in lack of interpretability for reasoning on incomplete knowledge graph (KG). |
| Approach: | They propose a new model that learns knowledge graph embeddings that can capture relation compositions by nature and reduces the solution space drastically. |
| Outcome: | The proposed model outperforms existing models and is comparable to or better than deep learning models such as ConvE. |
Leveraging LLM-GNN Integration for Open-World Question Answering over Knowledge Graphs (2026.eacl-long)
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| 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. |
COMBO: A Complete Benchmark for Open KG Canonicalization (2023.eacl-main)
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| Challenge: | Existing datasets for open KG canonicalization only provide gold entity-level canonization for noun phrases. |
| Approach: | They propose a complete benchmark for open KG canonicalization that provides gold ontology-level canonization for relation phrases and source sentences for extraction. |
| Outcome: | The proposed method improves relation canonicalization and ontology-level canonization of the noun phrase. |
Open Relation Modeling: Learning to Define Relations between Entities (2022.findings-acl)
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| Challenge: | Existing systems identify related entities but do not provide features for exploring relations between entities. |
| Approach: | They propose to teach machines to generate definition-like relation descriptions by letting them learn from defining entities. |
| Outcome: | The proposed model can generate definition-like relation descriptions that capture the representative characteristics of entities. |
MEKER: Memory Efficient Knowledge Embedding Representation for Link Prediction and Question Answering (2022.acl-srw)
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| 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. |
Towards Verifiable Generation: A Benchmark for Knowledge-aware Language Model Attribution (2024.findings-acl)
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| Challenge: | Existing evaluation metrics and benchmarks to attribute large language models to structured knowledge are lacking. |
| Approach: | They propose a task of Knowledge-aware Language Model Attribution that improves upon three core concerns with conventional attributed LMs. |
| Outcome: | The proposed model improves upon core concerns with conventional attributed LMs. |
PipeNet: Question Answering with Semantic Pruning over Knowledge Graphs (2024.starsem-1)
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| 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. |
Constraint-based Multi-hop Question Answering with Knowledge Graph (2022.naacl-industry)
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| 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. |
Guiding Neural Entity Alignment with Compatibility (2022.emnlp-main)
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| 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. |
CR-GIS: Improving Conversational Recommendation via Goal-aware Interest Sequence Modeling (2022.coling-1)
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| Challenge: | Existing methods to determine a goal item by sequentially tracking users’ interests ignore the rich goal-aware implicit interest sequence patterns in a dialog. |
| Approach: | They propose to model goal-aware implicit user interest sequence patterns in a dialog and a hierarchical Star Transformer to guide multi-turn utterances generation. |
| Outcome: | The proposed framework achieves more accurate recommendations with more fluent and coherent dialog utterances. |
CogMG: Collaborative Augmentation Between Large Language Model and Knowledge Graph (2024.acl-demos)
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| Challenge: | Large language models (LLMs) are susceptible to generating hallucinated content and often encompass factually inaccurate information. |
| Approach: | They propose a framework that leverages knowledge graphs to address the limitations of Large Language Models (LLMs) they identify and decompose required knowledge triples that are not present in the KG, enriching them and aligning updates with real-world demands. |
| Outcome: | The proposed framework reduces hallucinations and increases factual accuracy in QA scenarios while retaining the same quality of knowledge. |
Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment (2022.acl-long)
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Zijie Huang, Zheng Li, Haoming Jiang, Tianyu Cao, Hanqing Lu, Bing Yin, Karthik Subbian, Yizhou Sun, Wei Wang
| 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. |
Aligning Recommendation and Conversation via Dual Imitation (2022.emnlp-main)
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| Challenge: | Existing conversational recommendation systems ignore the advantage of user interest shift in connecting recommendation and conversation, leading to an ineffective loose coupling structure. |
| Approach: | They propose a dual imitation to explicitly align recommendation and conversation paths . they propose to generate high-quality responses with accurate recommendations and coherent explanations . |
| Outcome: | The proposed model outperforms the state-of-the-art models on recommendation and conversation performance with automatic, human, and novel explainability metrics. |
Conceptual Diagnostics for Knowledge Graphs and Large Language Models (2025.acl-industry)
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| Challenge: | Xu et al., 2024) argue that LLMs can be learned via conceptual consistency. |
| Approach: | They propose a method that takes concept hierarchies from a knowledge graph and generates benchmarks that test conceptual consistency in LLMs. |
| Outcome: | The proposed method reveals rates of conceptual inconsistencies in several state-of-the-art LLMs. |
OpenFact: Factuality Enhanced Open Knowledge Extraction (2023.tacl-1)
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Linfeng Song, Ante Wang, Xiaoman Pan, Hongming Zhang, Dian Yu, Lifeng Jin, Haitao Mi, Jinsong Su, Yue Zhang, Dong Yu
| Challenge: | Existing OIE systems organize knowledge into subject-relation-object (SRO) triplets, and they use templates to extract such knowledge triplet. |
| Approach: | They propose a framework to handle expressiveness and groundedness in OpenFact . they propose to use templates, extra constraints, and adopt human efforts to ensure that most triplets contain enough details. |
| Outcome: | The proposed framework improves expressiveness and groundedness of OpenFact . it is more accurate and denser than OPIEC-Linked, which is grounded to Wikidata . |
ESG-KG: A Multi-modal Knowledge Graph System for Automated Compliance Assessment (2026.eacl-demo)
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| Challenge: | Existing methods for ESG compliance assessment rely on fact-based retrieval methods. |
| Approach: | They propose a multi-modal information extraction pipeline to extract, structure, and evaluate sustainability reports. |
| Outcome: | The proposed system extracts, structures, and evaluates ESG-related content from text, tables, figures, and infographics. |
ConceptBert: Concept-Aware Representation for Visual Question Answering (2020.findings-emnlp)
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| Challenge: | Visual Question Answering (VQA) is a challenging task that has received increasing attention from both the computer vision and the natural language processing communities. |
| Approach: | They propose an algorithm which learns a joint Concept-Vision-Language embedding for questions which require common sense knowledge from external structured content. |
| Outcome: | The proposed model is based on the Outer Knowledge-VQA and VQA datasets. |
Keyphrase Generation with Fine-Grained Evaluation-Guided Reinforcement Learning (2021.findings-emnlp)
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| Challenge: | Existing KG evaluation metrics are only aware of the exact correctness of predictions on phrase-level and ignore semantic similarities between similar predictions and targets, which inhibits the model from learning deep linguistic patterns. |
| Approach: | They propose a fine-grained evaluation metric to improve the previous KG framework . the evaluation metrics are only aware of the exact correctness of predictions on phrase-level . |
| Outcome: | The proposed method outperforms the existing frameworks among all evaluation scores. |
QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering (2021.naacl-main)
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| Challenge: | Existing question answering systems lack the ability to access relevant knowledge and reason over it. |
| Approach: | They propose a model that uses KGs to identify relevant knowledge in QA contexts and perform joint reasoning over them. |
| Outcome: | The proposed model improves on the CommonsenseQA and OpenBookQA datasets and performs interpretable and structured reasoning. |
SyntheT2C: Generating Synthetic Data for Fine-Tuning Large Language Models on the Text2Cypher Task (2025.coling-main)
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| Challenge: | Existing efforts to bolster LLMs’ proficiency in Cypher generation are hindered by the lack of annotated datasets of Query-Cypher pairs. |
| Approach: | They propose a method for constructing a synthetic Query-Cypher pair dataset using LLM prompting and template-filling. |
| Outcome: | The proposed method enhances the performance of LLMs on Text2Cypher task via SFT. |
Knowledge Graph Embeddings in Geometric Algebras (2020.coling-main)
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| Challenge: | Existing knowledge graph embedding approaches model entities and relations in KGs using real-valued, complex-value, or hypercomplex-value representations. |
| Approach: | They propose a geometric algebra-based KG embedding framework which uses multivector representations and the geometric product to model entities and relations. |
| Outcome: | The proposed framework outperforms state-of-the-art models for link prediction. |
StATIK: Structure and Text for Inductive Knowledge Graph Completion (2022.findings-naacl)
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Elan Markowitz, Keshav Balasubramanian, Mehrnoosh Mirtaheri, Murali Annavaram, Aram Galstyan, Greg Ver Steeg
| 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. |
Conversational Question Answering with Language Models Generated Reformulations over Knowledge Graph (2024.findings-acl)
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| 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). |
KGPool: Dynamic Knowledge Graph Context Selection for Relation Extraction (2021.findings-acl)
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Abhishek Nadgeri, Anson Bastos, Kuldeep Singh, Isaiah Onando Mulang’, Johannes Hoffart, Saeedeh Shekarpour, Vijay Saraswat
| 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. |
Accelerating Antibiotic Discovery with Large Language Models and Knowledge Graphs (2025.acl-industry)
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| Challenge: | Antimicrobial resistance (AMR) is one of the top ten global public health threats . pharmaceutical industries face high costs (over $1 billion), long timelines, and a high failure rate . |
| Approach: | They propose an LLM-based pipeline that acts as an alert system, detecting prior evidence of antibiotic activity to prevent costly rediscoveries. |
| Outcome: | The proposed system integrates literature on organisms and chemicals into a Knowledge Graph (KG), ensuring taxonomic resolution, synonym handling, and multi-level evidence classification. |
Food Knowledge Representation Learning with Adversarial Substitution (2022.aacl-main)
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| Challenge: | Knowledge graph embedding (KGE) has been well-studied in general domains, but has not been examined for food computing. |
| Approach: | They propose to use a pre-trained language model to encode entities and relations, emphasizing contextual information in food KGs. |
| Outcome: | The proposed method is able to generate high quality substitutions over a food knowledge graph and provide generalized substitutions to meet different user needs. |
A System for Answering Simple Questions in Multiple Languages (2023.acl-demo)
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| Challenge: | Existing knowledge graph question answering systems are limited to simple questions, but they can be used to answer complex questions. |
| Approach: | They propose a multilingual Knowledge Graph Question Answering technique that orders potential responses based on the distance between the question’s text embeddings and the answer’s graph embedds. |
| Outcome: | The proposed method consistently outperforms baseline systems, including seq2seq QA models and complex rule-based pipelines. |
Mitigating Hallucination by Integrating Knowledge Graphs into LLM Inference – a Systematic Literature Review (2025.acl-srw)
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| 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. |
Cross-lingual Entity Alignment with Incidental Supervision (2021.eacl-main)
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| Challenge: | Existing methods to match entities in multilingual knowledge graphs are insufficient, resulting in inconsistent seed alignment between KGs. |
| Approach: | They propose a model that integrates multilingual KGs and monolingual text corpora in a shared embedding scheme and a self-learning based alignment learning process to induce correspondence between entities and lexemes. |
| Outcome: | The proposed model significantly outperforms state-of-the-art methods on benchmark datasets and significantly outpersts existing methods. |
Bring Your Own KG: Self-Supervised Program Synthesis for Zero-Shot KGQA (2024.findings-naacl)
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| Challenge: | a universal question-answering system that can operate on any knowledge graph is presented . previous work that relied on training data to query structured data stores is unrealistic . |
| Approach: | They propose a universal question-answering system that can operate on any knowledge graph . they use an LLM-backed symbolic agent to generate query-program exemplars . |
| Outcome: | The proposed system outperforms state-of-the-art model on domain-specific KGs by 7.08 F1 . the proposed system can be ready to use within a day, the authors show . |
Thesis Proposal: Stability-Aware, Evidence-Grounded Knowledge Graph for Substance Use Disorders and Social Determinants of Health (2026.eacl-srw)
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| Challenge: | Existing methods for NER and RE annotation are costly and difficult to scale. |
| Approach: | They propose a semantic stability framework for constructing explainable KGs using NER and RE annotations. |
| Outcome: | The proposed framework supports multi-hop reasoning, triadic SUD–SDOH–SUD mediation patterns, and feedback loop analysis. |
TopKG: Target-oriented Dialog via Global Planning on Knowledge Graph (2022.coling-1)
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| Challenge: | Existing target-oriented dialogs take a local and greedy strategy for response generation, where global planning is absent. |
| Approach: | They propose a global planning method for target-oriented dialog on a commonsense knowledge graph to adjust local response generation towards the global target. |
| Outcome: | The proposed method can reach the target with a higher success rate, fewer turns, and more coherent responses. |
Advancing Abductive Reasoning in Knowledge Graphs through Complex Logical Hypothesis Generation (2024.acl-long)
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| 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. |
IndiFoodVQA: Advancing Visual Question Answering and Reasoning with a Knowledge-Infused Synthetic Data Generation Pipeline (2024.findings-eacl)
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| Challenge: | Large Vision Language Models lack domain-specific data for reasoning on complex problems. |
| Approach: | They propose to use explicit knowledge-infused questions, answers, and reasons to answer and reason upon the questions. |
| Outcome: | The proposed model improves by 25% over the baseline model. |
A New Pipeline for Knowledge Graph Reasoning Enhanced by Large Language Models Without Fine-Tuning (2024.emnlp-main)
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| 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. |
OpenDialKG: Explainable Conversational Reasoning with Attention-based Walks over Knowledge Graphs (P19-1)
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| Challenge: | Existing models that use a large-scale knowledge graph to create a conversational reasoning model are domain-agnostic and scalable. |
| Approach: | They propose a conversational reasoning model that strategically traverses through a large-scale common fact knowledge graph to introduce engaging and contextually diverse entities and attributes. |
| Outcome: | The proposed model retrieves more natural responses than state-of-the-art models in both in-domain and cross-domain tasks. |
CoSe-Co: Text Conditioned Generative CommonSense Contextualizer (2022.naacl-main)
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| 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. |
TranS: Transition-based Knowledge Graph Embedding with Synthetic Relation Representation (2022.findings-emnlp)
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| Challenge: | Knowledge graph embedding (KGE) is a computational approach to learn continuous vector representations of relations and entities in knowledge graphs. |
| Approach: | They propose a transition-based method to learn continuous vector representations of relations and entities in knowledge graph (KG) it replaces a single relation vector in the relation part with a synthetic relation representation with entity-relation interactions to solve these problems. |
| Outcome: | The proposed method achieves state-of-the-art on a large knowledge graph dataset. |
Dangling-Aware Entity Alignment with Mixed High-Order Proximities (2022.findings-naacl)
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Juncheng Liu, Zequn Sun, Bryan Hooi, Yiwei Wang, Dayiheng Liu, Baosong Yang, Xiaokui Xiao, Muhao Chen
| Challenge: | Existing methods for dangling-aware entity alignment are underexplored but important problem. |
| Approach: | They propose a framework that uses high-order proximities to detect dangling entities and align matchable entities. |
| Outcome: | The proposed framework detects dangling entities and aligns matchable entities better than existing methods. |
ENT-DESC: Entity Description Generation by Exploring Knowledge Graph (2020.emnlp-main)
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| Challenge: | Existing models for knowledge-to-text generation use RDF triples or key-value pairs to generate a natural language description. |
| Approach: | They propose a large-scale dataset to facilitate the study of KG-to-text . they propose MGCN model architecture that incorporates aggregation methods to extract the rich graph information. |
| Outcome: | The proposed model can represent the original graph information more comprehensively and integrates multiple aggregation methods to extract the rich graph information. |
COSIGN: Contextual Facts Guided Generation for Knowledge Graph Completion (2024.naacl-long)
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| Challenge: | Existing methods for knowledge graph completion (KGC) are limited in generality and scalability due to poor contextual facts. |
| Approach: | They propose a contextual facts collector and contextual facts organizer to enhance the inference ability of GM-based methods for various KGC tasks. |
| Outcome: | The proposed model outperforms state-of-the-art methods in terms of performance. |
Syntax Controlled Knowledge Graph-to-Text Generation with Order and Semantic Consistency (2022.findings-naacl)
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| Challenge: | Existing knowledge graph-to-text generation methods focus on sequence-to sequence generation, but the linearized order of KG is obtained through a heuristic search without data-driven optimization. |
| Approach: | They propose to generate easy-to-understand sentences from the knowledge graph . they incorporate part-of-speech syntactic tags to constrain the positions to copy words from the KG and employ a semantic context scoring function to evaluate the semantic fitness for each word in its local context. |
| Outcome: | The proposed method achieves state-of-the-art on two datasets, WebNLG and DART, and achieves high consistency. |
Integration of Knowledge Graph Embedding Into Topic Modeling with Hierarchical Dirichlet Process (N19-1)
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| Challenge: | Topic models are used to extract topical structures from document-word frequency representations of the text corpus without supervision. |
| Approach: | They propose a Bayesian nonparametric topic modeling with knowledge graph embedding to employ knowledge graphs to extract more coherent topics. |
| Outcome: | The proposed model performs better on three public datasets than state-of-the-art models on topic coherence and document classification accuracy. |
Fusing Context Into Knowledge Graph for Commonsense Question Answering (2021.findings-acl)
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| Challenge: | Existing methods to combine language modeling and knowledge graphs (KG) lack the context to provide a more precise understanding of the concepts. |
| Approach: | They propose to use external entity descriptions to provide contextual information for commonsense question answering models. |
| Outcome: | The proposed model achieves state-of-the-art among non-generative models in OpenBookQA and is the first of its kind in the field. |
Exclusive Hierarchical Decoding for Deep Keyphrase Generation (2020.acl-main)
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| Challenge: | Existing approaches to generate keyphrases ignore hierarchical compositionality of keyphrase set and generate duplicated keyphrase sets. |
| Approach: | They propose a hierarchical decoding framework that explicitly models hierarchic compositionality of a keyphrase set and either a soft or a hard exclusion mechanism to enhance the diversity of the generated keyphrases. |
| Outcome: | The proposed framework generates less duplicated and more accurate keyphrases on a set of keyphrase sets. |
AutoETER: Automated Entity Type Representation for Knowledge Graph Embedding (2020.findings-emnlp)
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| Challenge: | Existing knowledge graphs are incomplete whether they are constructed manually or automatically, limiting the effectiveness when exploited for downstream applications. |
| Approach: | They propose a KGE framework with an automatic type embedding mechanism which can be easily integrated into any existing KGE model. |
| Outcome: | The proposed model can model and infer all the relation patterns and complex relations compared to state-of-the-art models on four datasets. |
What Breaks Knowledge Graph based RAG? Benchmarking and Empirical Insights into Reasoning under Incomplete Knowledge (2026.eacl-long)
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Dongzhuoran Zhou, Yuqicheng Zhu, Xiaxia Wang, Hongkuan Zhou, Yuan He, Jiaoyan Chen, Steffen Staab, Evgeny Kharlamov
| Challenge: | Existing evaluation metrics and lenient answer matching criteria obscure meaningful comparisons. |
| Approach: | They propose a general method for constructing benchmarks and a method to assess KG-RAG methods under incomplete knowledge. |
| Outcome: | The proposed method systematically assesses KG-RAG methods under incomplete knowledge. |
Generalization in Text-based Games via Hierarchical Reinforcement Learning (2021.findings-emnlp)
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| Challenge: | Reinforcement Learning (RL) based agents are promising for text-based games, but their generalization remains a challenge. |
| Approach: | They propose a hierarchical framework for reinforcement learning based on knowledge graphs . they propose to decompose the game into subtasks and execute a sub-policy in the low level to conduct goal-conditioned reinforcement learning. |
| Outcome: | The proposed framework enjoys favorable generalizability on a set of difficulty levels and is able to handle complex training tasks. |
Dynamic Relevance Graph Network for Knowledge-Aware Question Answering (2022.coling-1)
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| Challenge: | Existing approaches to solve commonsense question answering problems often miss some edges between entities, which breaks the reasoning chain. |
| Approach: | They propose a graph neural network architecture that uses relevance as graph edges to establish new edges dynamically for learning node representations in the graph network. |
| Outcome: | The proposed approach shows competitive performance on two QA benchmarks, CommonsenseQA and OpenbookQA, compared to the state-of-the-art published results. |
DALK: Dynamic Co-Augmentation of LLMs and KG to answer Alzheimer’s Disease Questions with Scientific Literature (2024.findings-emnlp)
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Dawei Li, Shu Yang, Zhen Tan, Jae Baik, Sukwon Yun, Joseph Lee, Aaron Chacko, Bojian Hou, Duy Duong-Tran, Ying Ding, Huan Liu, Li Shen, Tianlong Chen
| Challenge: | Recent advances in large language models have achieved promising performances across various applications, but the challenge of integrating long-tail knowledge continues to impede the seamless adoption of LLMs in specialized domains. |
| Approach: | They propose a dynamic co-augmentation framework for the refinement of large language models and knowledge graphs in the context of Alzheimer's Disease. |
| Outcome: | The proposed framework can be used to study Alzheimer's Disease (AD) using LLMs and KGs. |
EventKE: Event-Enhanced Knowledge Graph Embedding (2021.findings-emnlp)
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| 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 . |
Adaptive Attentional Network for Few-Shot Knowledge Graph Completion (2020.emnlp-main)
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| Challenge: | Recent attempts to learn static representations of entities and references ignore their dynamic properties. |
| Approach: | They propose to learn static representations of entities and references ignoring their dynamic properties . a neighbor encoder learns entities' roles while a query-aware aggregator learns references' contributions . |
| Outcome: | The proposed approach achieves state-of-the-art results with different few-shot sizes. |
Semantic Structure Based Query Graph Prediction for Question Answering over Knowledge Graph (2022.coling-1)
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| Challenge: | Existing approaches for query graph generation ignore the semantic structure of a question . Existing methods ignore the structure of the question, resulting in noisy query graph candidates. |
| Approach: | They propose to build query graphs from natural language questions to predict semantic structure of a question. |
| Outcome: | The proposed method can predict the semantic structure of a question using six semantic structures from common questions in KGQA. |
Few-shot Knowledge Graph-to-Text Generation with Pretrained Language Models (2021.findings-acl)
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| Challenge: | Existing models for KG-to-text generation are based on pretrained language models. |
| Approach: | They propose to automatically generate a text that describes the facts in knowledge graph (KG) they leverage the excellent capacities of pretrained language models (PLMs) in language understanding and generation. |
| Outcome: | The proposed model outperforms all comparison methods on fully-supervised and fewshot settings. |
TeRo: A Time-aware Knowledge Graph Embedding via Temporal Rotation (2020.coling-main)
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| Challenge: | Existing knowledge graphs that contain time information for entities and relations have been used for learning and inference. |
| Approach: | They propose a temporal evolution of entity embedding that defines the temporal rotation from the initial time to the current time in the complex vector space. |
| Outcome: | The proposed model outperforms existing state-of-the-art models for link prediction on three different TKGs. |
HyperFM: Fact-Centric Multimodal Fusion for Link Prediction over Hyper-Relational Knowledge Graphs (2025.acl-long)
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| Challenge: | Existing link prediction techniques focus on learning the complex relationships between entities and relations while ignoring the multimodal information. |
| Approach: | They propose a fact-centric fusion technique that captures complex interactions between different data modalities while accommodating the hyper-relational structure of the KG in a facts-centric manner. |
| Outcome: | The proposed technique improves on two real-world KG datasets by 6.0-6.8% over baselines. |
CORN: Co-Reasoning Network for Commonsense Question Answering (2022.coling-1)
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| Challenge: | Existing work uses two independent modules to model QA content and external commonsense knowledge graph (KG) Existing research uses two separate modules to create QA contextual text representations and relationships between QA entities. |
| Approach: | They propose a commonsense question answering (QA) model that uses two independent modules to model QA contextual text representation and relationships between QA entities in KG. |
| Outcome: | The proposed model achieves state-of-the-art on QA benchmarks in the CommonsenseQA and OpenBookQA datasets. |
Diversifying Content Generation for Commonsense Reasoning with Mixture of Knowledge Graph Experts (2022.findings-acl)
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| Challenge: | Recent years have seen a surge of interest in improving the generation quality of commonsense reasoning tasks. |
| Approach: | They propose a method that diversifies the generative reasoning by a mixture of expert strategy on commonsense knowledge graphs to encourage various generation outputs. |
| Outcome: | The proposed method improves diversity while achieving on par performance on two GCR benchmarks, based on both automatic and human evaluations. |
TwiRGCN: Temporally Weighted Graph Convolution for Question Answering over Temporal Knowledge Graphs (2023.eacl-main)
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| Challenge: | Recent years have witnessed interest in Temporal Question Answering over Knowledge Graphs (TKGQA) but these methods are highly engineered and do not automatically discover relevant parts of the KG during multi-hop reasoning. |
| Approach: | They propose a scheme to modulate the messages passed through a KG edge during convolution based on the relevance of its associated period to the question. |
| Outcome: | The proposed system outperforms state-of-the-art models on a recent challenging dataset for multi-hop complex temporal QA called TimeQuestions. |
KACC: A Multi-task Benchmark for Knowledge Abstraction, Concretization and Completion (2021.findings-acl)
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| Challenge: | Existing studies focus on partial aspects of knowledge abstraction, concretization, and completion (KACC). |
| Approach: | They propose a unified knowledge graph benchmark to improve existing benchmarks . they collect new datasets that contain larger concept graphs and cross-view links . |
| Outcome: | The proposed benchmark improves existing benchmarks in terms of dataset scale, task coverage, and difficulty. |
Worldly Wise (WoW) - Cross-Lingual Knowledge Fusion for Fact-based Visual Spoken-Question Answering (2021.naacl-main)
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| 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. |
Multi-Task Learning for Knowledge Graph Completion with Pre-trained Language Models (2020.coling-main)
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| 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. |
Noisy Positive-Unlabeled Learning with Self-Training for Speculative Knowledge Graph Reasoning (2023.findings-acl)
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Ruijie Wang, Baoyu Li, Yichen Lu, Dachun Sun, Jinning Li, Yuchen Yan, Shengzhong Liu, Hanghang Tong, Tarek Abdelzaher
| Challenge: | State-of-the-art methods fail in speculative reasoning task on knowledge graphs . state-of the-art approaches assume correctness of fact is determined by its presence in KG . |
| Approach: | They propose a speculative reasoning task on real-world knowledge graphs . they propose nPUGraph that estimates correctness of both collected and uncollected facts . |
| Outcome: | The proposed framework improves the robustness of a label posterior-aware graph encoder against false positive links and identifies missing facts to provide high-quality grounds of reasoning. |
Modularized Transfer Learning with Multiple Knowledge Graphs for Zero-shot Commonsense Reasoning (2022.naacl-main)
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| Challenge: | Currently, commonsense reasoning systems are limited by expensive data annotations and overfitting to a specific benchmark. |
| Approach: | They propose to transform a commonsense knowledge graph into synthetic QA-form samples for model training. |
| Outcome: | The proposed framework improves performance with multiple commonsense KGs on five commonsensense reasoning benchmarks. |
End-to-End Construction of NLP Knowledge Graph (2021.findings-acl)
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| Challenge: | a new schema for NLP knowledge about tasks, datasets and metrics is proposed. |
| Approach: | They propose a new schema that represents knowledge about tasks, datasets and metrics in the NLP domain. |
| Outcome: | The proposed framework can be automatically built into scientific leaderboards . the proposed system achieves reasonable results for all relation types on this small-scale graph . |
Variational Knowledge Graph Reasoning (N18-1)
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| Challenge: | Existing knowledge graphs have large amount of missing links, which limits their application . a recent study has proposed to design an automated inference model to complete the missing links in large knowledge graph. |
| Approach: | They propose to use variation inference to solve missing links in knowledge graphs . they use a posterior approximator, prior (path finder) and likelihood (path reasoner) |
| Outcome: | The proposed model achieves state-of-the-art on multiple datasets and is highly accurate. |
ELDEN: Improved Entity Linking Using Densified Knowledge Graphs (N18-1)
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| Challenge: | Entity Linking (EL) systems aim to automatically map mentions of an entity in text to the corresponding entity in Knowledge Graph (KG). |
| Approach: | They propose to densify the Knowledge Graph (KG) with co-occurrence statistics and then use the densified KG to train entity embeddings. |
| Outcome: | The proposed system outperforms state-of-the-art EL systems on benchmark datasets and outperformed state- of-the art systems on sparsely connected entities in the KG. |
Neural Path Hunter: Reducing Hallucination in Dialogue Systems via Path Grounding (2021.emnlp-main)
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| Challenge: | Dialogue systems that generate factually incorrect responses are often unfitful and hallucinate factuality invalid. |
| Approach: | They propose a method to improve faithfulness and reduce hallucination of neural dialogue systems to known facts supplied by a Knowledge Graph. |
| Outcome: | The proposed approach improves faithfulness and reduces hallucination of dialogue systems to known facts . it leverages a token-level fact critic to identify plausible sources of hallucinism . |
ReaRev: Adaptive Reasoning for Question Answering over Knowledge Graphs (2022.findings-emnlp)
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| Challenge: | Knowledge Graph Question Answering (KGQA) involves retrieving entities as answers from a Knowledge Flow using natural language queries. |
| Approach: | They propose a method to decode a question into instructions that are dense question representations used to guide the KG traversals. |
| Outcome: | The proposed method improves instruction decoding and execution by using a KG-aware information to update the initial instructions. |
Few-shot Knowledge Graph Relational Reasoning via Subgraph Adaptation (2024.naacl-long)
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| Challenge: | Existing methods to predict unseen triplets from knowledge graphs are limited by spurious information in KGs. |
| Approach: | They propose a framework that adapts contextualized graphs to subgraphs generated from support and query triplets to perform the prediction. |
| Outcome: | The proposed framework extracts more comprehensive information from support triplets while minimizing spurious information when predicting query triplet. |
Semantic Parsing for Conversational Question Answering over Knowledge Graphs (2023.eacl-main)
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| Challenge: | Recent years have seen an increasing number of applications aiming to build conversational interfaces based on information retrieval and user recommendation. |
| Approach: | They develop a dataset where user questions are annotated with Sparql parses and system answers correspond to execution results thereof. |
| Outcome: | The proposed parsers can be used to ground questions into queries over definitions in a knowledge graph with large vocabularies. |
Identify, Align, and Integrate: Matching Knowledge Graphs to Commonsense Reasoning Tasks (2021.eacl-main)
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| Challenge: | Empirically, we investigate KG matches for the SocialIQA, Physical IQA, and MCScript2.0 datasets with 3 diverse KGs: ATOMIC (SIQA), ConceptNet (Speer et al., 2017), and an automatically constructed instructional KG based on WikiHow (Ostermann e., 2019b). |
| Approach: | They propose a method to assess how well a candidate KG can fill in knowledge gaps for a given task by using commonsense probes. |
| Outcome: | Empirically, we show that the proposed KG-to-task match is a good match for socialIQA, physical IQA, and MCScript2.0 datasets with 3 diverse KGs: ATOMIC, ConceptNet, and an instructional KG based on WikiHow. |
SKA-Bench: A Fine-Grained Benchmark for Evaluating Structured Knowledge Understanding of LLMs (2025.findings-emnlp)
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| Challenge: | Existing evaluations for Structured Knowledge (SK) understanding are non-rigorous and focus on a single type of SK. |
| Approach: | They propose a structured knowledge understanding benchmark that includes four widely used structured knowledge forms. |
| Outcome: | The proposed benchmark is based on four widely used structured knowledge forms . it includes a question, an answer, positive knowledge units, and noisy knowledge units . |
SimCKP: Simple Contrastive Learning of Keyphrase Representations (2023.findings-emnlp)
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| Challenge: | Existing models for keyphrase generation and keyphrase extraction use a token level to generate keyphrases that do not appear in a document. |
| Approach: | They propose a simple contrastive learning framework that generates keyphrases that do not appear in a document and a reranker that adapts the scores for each generated phrase. |
| Outcome: | The proposed model outperforms the state-of-the-art models on multiple benchmark datasets. |
Sequence-to-Sequence Knowledge Graph Completion and Question Answering (2022.acl-long)
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| Challenge: | Knowledge graph embedding (KGE) models represent each entity and relation of a knowledge graph (KG) with low-dimensional embeddable vectors. |
| Approach: | They propose to use an off-the-shelf encoder-decoder Transformer model to generate a knowledge graph embedding model that can be used for KG link prediction and incomplete KG question answering. |
| Outcome: | The proposed model outperforms baselines on multiple large-scale datasets without extensive hyperparameter tuning. |
Learning to Ask: Multi-Decoder Fine-Tuning for Multi-Hop Visual Question Generation with External Knowledge (2026.findings-eacl)
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| Challenge: | Traditional supervised QG methods rely on tokenlevel alignment with fixed gold labels struggle to capture diverse valid question formulations. |
| Approach: | They propose a model-agnostic framework that integrates multimodal inputs with a multi-decoder architecture to optimize for multiple labels per sample. |
| Outcome: | The proposed framework improves fluency, reasoning depth, and relevance of visual questions. |
KGHaluBench: A Knowledge Graph-Based Hallucination Benchmark for Evaluating the Breadth and Depth of LLM Knowledge (2026.findings-eacl)
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| Challenge: | Existing benchmarks for large language models are limited by static and narrow questions, leading to limited coverage and misleading evaluations. |
| Approach: | They propose a Knowledge Graph-based hallucination benchmark that assesses Large Language Models across the breadth and depth of their knowledge and provides a fairer and more comprehensive insight into LLM truthfulness. |
| Outcome: | The proposed framework assesses LLMs across breadth and depth of their knowledge, and provides a fairer and more comprehensive insight into LLM truthfulness. |
Structure-Augmented Keyphrase Generation (2021.emnlp-main)
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| Challenge: | Creating keyphrases that are likely to be words absent from the given document is challenging . |
| Approach: | They propose novel keyphrase generation tasks that augment missing context by adding keyphrases to documents. |
| Outcome: | The proposed keyphrase generation task outperforms the state-of-the-art in two keyphrase tasks. |
Zero-shot Medical Entity Retrieval without Annotation: Learning From Rich Knowledge Graph Semantics (2021.findings-acl)
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| Challenge: | Current approaches to medical entity retrieval generalize poorly to unseen sub-specialties . zero-shot retrieval is challenging due to the high degree of ambiguity and variability in medical corpora . |
| Approach: | They propose a set of learning tasks designed to train efficient zero-shot entity retrieval models. |
| Outcome: | The proposed architecture outperforms common zero-shot benchmarks with 7% to 30% higher recall across multiple major medical ontologies. |
Knowledge-Aware Query Expansion with Large Language Models for Textual and Relational Retrieval (2025.naacl-long)
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| Challenge: | Existing methods to generate query expansions focus on enhancing textual similarities between search queries and document corpus, overlooking document relations. |
| Approach: | They propose a knowledge-aware query expansion framework augmenting LLMs with structured document relations from knowledge graph (KG) they leverage document texts as rich KG node representations and use document-based relation filtering for their method. |
| Outcome: | The proposed framework augments LLMs with structured document relations from knowledge graph (KG) Extensive experiments on three datasets of diverse domains show the advantages compared against state-of-the-art methods on textual and relational semi-structured retrieval. |
Retrieval, Reasoning, Re-ranking: A Context-Enriched Framework for Knowledge Graph Completion (2025.naacl-long)
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| Challenge: | Existing embedding-based methods rely on triples in the KG, which is vulnerable to specious relation patterns and long-tail entities. |
| Approach: | They propose a context-enriched framework for KGC that uses a large language model to generate potential answers for each query triple. |
| Outcome: | The proposed framework improves on FB15k237 and WN18RR datasets. |
One-Shot Relational Learning for Knowledge Graphs (D18-1)
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| 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. |
Ruleformer: Context-aware Rule Mining over Knowledge Graph (2022.coling-1)
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| Challenge: | Existing work on rule mining focuses on mining rules, but how to select appropriate rules for completion of different triplets has not been discussed. |
| Approach: | They propose to take context information into consideration when selecting suitable rules . they devise a transformer-based rule mining approach, Ruleformer . |
| Outcome: | The proposed model takes context information into consideration, which helps select suitable rules for inference tasks. |
HyTE: Hyperplane-based Temporally aware Knowledge Graph Embedding (D18-1)
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| Challenge: | Existing KG embedding methods ignore this temporal dimension while learning embedds of the KG elements. |
| Approach: | They propose a temporally aware KG embedding method which incorporates time in the entity-relation space by associating each timestamp with a corresponding hyperplane. |
| Outcome: | The proposed method performs KG inference using temporal guidance and predicts scopes for relational facts with missing time annotations. |
ReasoningLM: Enabling Structural Subgraph Reasoning in Pre-trained Language Models for Question Answering over Knowledge Graph (2023.emnlp-main)
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| Challenge: | Question Answering over Knowledge Graph (KGQA) aims to find answer entities for natural language questions based on knowledge graphs. |
| Approach: | They propose a subgraph-aware self-attention mechanism to imitate the graph neural network (GNN) based module to perform multi-hop reasoning on KG. |
| Outcome: | The proposed method surpasses state-of-the-art models by a large margin even with fewer updated parameters and less training data. |
KGE-CL: Contrastive Learning of Tensor Decomposition Based Knowledge Graph Embeddings (2022.coling-1)
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| Challenge: | Existing knowledge graph embedding methods ignore semantic similarity between related entities and entity-relation couples in different triples . |
| Approach: | They propose a contrastive learning framework for tensor decomposition based (TDB) KGE that can shorten the semantic distance of related entities and entity-relation couples in different triples and thus improve the performance of KGE. |
| Outcome: | The proposed method achieves 51.2% MRR, 46.8% Hits@1 on three standard KGE datasets, 37.8% MRR and 28.6% Hits @1 on FB15k-237 datasets and 59.1% MRR . |
Building MUSCLE, a Dataset for MUltilingual Semantic Classification of Links between Entities (2024.lrec-main)
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| Challenge: | In this paper we present a dataset for MUltilingual Lexical Relation Classification (LRC) systems with 27K pairs of universal concepts selected from Wikidata, a large and highly multilingual factual Knowledge Graph (KG). |
| Approach: | They propose a dataset for MUltilingual lexico-semantic Classification of Links between Entities using 27K pairs of universal concepts selected from Wikidata. |
| Outcome: | The proposed dataset bridges lexical and conceptual semantics, avoids linguistic memorization, is domain-balanced across entities, and enables enrichment and hierarchical information retrieval. |
CAGK: Collaborative Aspect Graph Enhanced Knowledge-based Recommendation (2024.lrec-main)
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| Challenge: | Existing KG-based recommendations have low link rates, redundant knowledge in KG, and low ratings and negative aspect sentiment. |
| Approach: | They propose a model that integrates auxiliary information such as social networks, user or item attributes, images, contextual data, etc. |
| Outcome: | The proposed model improves on two widely used benchmark datasets, Amazon-book and Yelp2018. |
Open-Domain Contextual Link Prediction and its Complementarity with Entailment Graphs (2021.findings-emnlp)
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| Challenge: | Existing methods for linking knowledge graphs only use textual contexts . contextual link prediction is useful for finding context-dependent entailments . |
| Approach: | They propose a task of open-domain contextual link prediction which uses textual context and KG structure to perform link prediction. |
| Outcome: | The proposed model can ground the triples in the context of the original dataset and infer missing relations in context. |
Knowledge Graph Unlearning with Schema (2025.coling-main)
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| Challenge: | Unlearning on knowledge graphs has not been extensively studied. |
| Approach: | They propose a new unlearning method based on schema for knowledge graph (KG) they update the representation of the deleted element’s neighborhood with an unlearning object that regulates the affinity between the affected neighborhood and the instances within the same schema. |
| Outcome: | The proposed method is evaluated on various KG embedding models with benchmark datasets. |
Knowledge Graph Embedding Compression (2020.acl-main)
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| Challenge: | Knowledge graph (KG) embedding techniques that learn continuous embedds of entities and relations consume a large amount of storage and memory. |
| Approach: | They propose a method that compresses the KG embedding layer by representing each entity in the KA as a vector of discrete codes and then composes the embeddables from these codes. |
| Outcome: | The proposed approach achieves 50-1000x compression of embeddings with a minor loss in performance on standard KG embeddable evaluations and retains the ability to perform reasoning tasks such as KG inference. |
Neural Compositional Denotational Semantics for Question Answering (D18-1)
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| Challenge: | a new model for compositional questions is needed to answer multi-step reasoning . the model is inspired by formal approaches to compositional semantics . |
| Approach: | They propose an end-to-end differentiable model for interpreting compositional questions . they build a latent tree of interpretable expressions over a sentence . |
| Outcome: | The proposed model outperforms RNN encoders when test questions are longer than training questions. |
Reducing Hallucinations in Language Model-based SPARQL Query Generation Using Post-Generation Memory Retrieval (2026.findings-eacl)
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| Challenge: | Large language models (LLMs) are susceptible to hallucinations and out-of-distribution errors when generating KG elements, such as Uniform Resource Identifiers (URIs). |
| Approach: | They propose a SPARQL query-generating framework that uses natural language placeholders and a non-parametric memory module to retrieve and resolve the correct KG URIs. |
| Outcome: | The proposed framework significantly enhances query correctness across various LLMs, datasets, and distribution shifts while achieving the near-complete suppression of URI hallucinations. |
Faithfully Explainable Recommendation via Neural Logic Reasoning (2021.naacl-main)
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| Challenge: | Existing models for explainable recommendation have neglected faithfulness of KG reasoning . |
| Approach: | They propose to draw on interpretable logical rules to guide path-reasoning process for explanation generation. |
| Outcome: | The proposed method delivers high-quality recommendations and ascertains the faithfulness of the derived explanation. |
KGVL-BART: Knowledge Graph Augmented Visual Language BART for Radiology Report Generation (2023.eacl-main)
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| Challenge: | Timely generation of radiology reports and diagnoses is a challenge worldwide due to the enormous number of cases and shortage of radiologists. |
| Approach: | They propose a Knowledge Graph Augmented Vision Language BART model that takes two chest X-ray images and outputs a report with patient-specific findings. |
| Outcome: | The proposed model outperforms state-of-the-art transformer-based models on scoring metrics. |
Label-Free Distant Supervision for Relation Extraction via Knowledge Graph Embedding (D18-1)
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| Challenge: | Existing methods to generate large scale labeled data for relation extraction produce noisy relation labels when there are multiple relationships between entities. |
| Approach: | They propose a method which assumes that a pair of entities appears in a Knowledge Graph and trains a relation classifier. |
| Outcome: | The proposed method performs well in the current distant supervision dataset. |
Incorporating Graph Attention Mechanism into Knowledge Graph Reasoning Based on Deep Reinforcement Learning (D19-1)
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| Challenge: | Existing methods for learning knowledge Graphs are incomplete and therefore need well-pretraining. |
| Approach: | They propose a deep reinforcement learning based model which incorporates LSTM and Graph Attention Mechanism as the memory components. |
| Outcome: | The proposed model can get rid of the pretraining process and achieve state-of-the-art performance compared with the other models. |
Representation Learning with Ordered Relation Paths for Knowledge Graph Completion (D19-1)
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| Challenge: | Existing knowledge graphs are incomplete and lack the order of relations in paths. |
| Approach: | They propose a method which takes relation paths into account but ignores order of relations in paths which is important for reasoning. |
| Outcome: | The proposed method performs better than state-of-the-art methods on two benchmark datasets. |
ActiveEA: Active Learning for Neural Entity Alignment (2021.emnlp-main)
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| 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. |
RHO: Reducing Hallucination in Open-domain Dialogues with Knowledge Grounding (2023.findings-acl)
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| Challenge: | Existing knowledge-grounded dialogue systems generate accurate and informative responses, but they are prone to hallucination problems. |
| Approach: | They propose a method to generate hallucinated responses using knowledge graphs . they propose local knowledge grounding to combine textual embeddings with corresponding KG embeddments . a global knowledge ground technique is also proposed to equip RHO with multi-hop reasoning abilities . |
| Outcome: | The proposed approach outperforms state-of-the-art methods on automatic and human evaluation by a large margin. |
Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training (2021.naacl-main)
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| Challenge: | Existing work on data-to-text generation focused on domain-specific benchmark datasets. |
| Approach: | They use a KG-Wikipedia text aligned corpus to verbalize the entire English Wikidata KG . they show that this approach can be used to integrate structured KGs and natural language corpora . |
| Outcome: | The proposed method improves on open domain QA and the LAMA knowledge probe. |
AttnIO: Knowledge Graph Exploration with In-and-Out Attention Flow for Knowledge-Grounded Dialogue (2020.emnlp-main)
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| Challenge: | Existing models for retrieving proper knowledge relevant to conversational context use only KG structure . empirical evaluations present a marked performance improvement of AttnIO compared to all baselines in OpenDialKG dataset . |
| Approach: | They propose a dialog-conditioned path traversal model that makes full use of rich structural information in KG . they show a marked performance improvement compared to baselines in OpenDialKG a KG dataset . |
| Outcome: | The proposed model makes full use of rich structural information in KG structure . it can be trained to generate an adequate knowledge path even when paths are not available . |
PALT: Parameter-Lite Transfer of Language Models for Knowledge Graph Completion (2022.findings-emnlp)
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| Challenge: | Pretrained language models (LMs) are a powerful transfer learning approach for knowledge graph (KG) completion. |
| Approach: | They propose a parameter-lite transfer learning approach for pretrained language models for knowledge graph (KG) completion. |
| Outcome: | The proposed model outperforms the state-of-the-art models on a knowledge graph completion benchmark by tuning 1% of the parameters. |
Enhancing Contextual Word Representations Using Embedding of Neighboring Entities in Knowledge Graphs (2022.coling-1)
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| 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. |
Hierarchical User Intent Inference with Knowledge Graph Grounding (2026.findings-eacl)
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| Challenge: | Existing large language models lack structured grounding and do not capture nuanced intent expression. |
| Approach: | They propose a Hierarchical Intent Inference framework that first predicts fine-grained aspect ratings and then generates natural language intent statements guided by contextual subgraphs retrieved from a domain-specific knowledge graph. |
| Outcome: | The proposed framework outperforms strong LLM and encoder-based baselines on a hotel review dataset. |
Multilingual Knowledge Graph Completion via Ensemble Knowledge Transfer (2020.findings-emnlp)
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| 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. |
An Integrated Approach for Keyphrase Generation via Exploring the Power of Retrieval and Extraction (N19-1)
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| Challenge: | Existing methods on keyphrase generation are purely extractive or generative . however, extractive methods cannot predict absent keyphrases which are not in the document. |
| Approach: | They propose a multi-task learning framework that jointly learns an extractive model and a generative model. |
| Outcome: | The proposed approach outperforms the state-of-the-art methods on five keyphrase generation tasks. |
Hyperbolic Temporal Knowledge Graph Embeddings with Relational and Time Curvatures (2021.findings-acl)
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| Challenge: | Existing knowledge Graph models for Link Prediction are insensitive to time. |
| Approach: | They propose a time-aware extension of ATTH model which defines curvature of a Riemannian manifold as the product of both relation and time. |
| Outcome: | The proposed model can achieve competitive or even better performance than the state-of-the-art model on Temporal KGs, albeit its nontemporality. |
Complex Question Answering on knowledge graphs using machine translation and multi-task learning (2021.eacl-main)
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Saurabh Srivastava, Mayur Patidar, Sudip Chowdhury, Puneet Agarwal, Indrajit Bhattacharya, Gautam Shroff
| Challenge: | Existing approaches to question answering on knowledge graphs are based on a modularized sequential approach where errors in one module lead to the accumulation of errors in downstream modules. |
| Approach: | They propose a multi-task BERT based Neural Machine Translation model to address these challenges. |
| Outcome: | The proposed model can answer questions over a knowledge graph on one publicly available and one proprietary dataset. |
Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network (P19-1)
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| Challenge: | Existing approaches to cross-lingual knowledge graph (KG) alignment rely on entity embeddings derived from monolingual KG structural information. |
| Approach: | They propose a topic entity graph to represent entities with contextual information in KGs. |
| Outcome: | The proposed model outperforms state-of-the-art methods by a large margin. |
CollabKG: A Learnable Human-Machine-Cooperative Information Extraction Toolkit for (Event) Knowledge Graph Construction (2024.lrec-main)
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| Challenge: | Existing IE tools lack multi-task support and automatic updates for KG and EKG construction. |
| Approach: | They propose a human-machine-cooperative IE toolkit for KG and EKG construction that unifies different IE subtasks and integrates LLMs as the assistant machine. |
| Outcome: | The proposed tool improves annotation quality, efficiency, and stability simultaneously. |
Faithful Inference Chains Extraction for Fact Verification over Multi-view Heterogeneous Graph with Causal Intervention (2025.coling-main)
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| Challenge: | Existing methods for fact verification do not extract faithful inference chains due to the diversity of relation paths. |
| Approach: | They propose a multi-view heterogeneous Graph with causal intervention to extract evidence graphs from the knowledge graph. |
| Outcome: | The proposed model provides precise evidence graphs and achieves state-of-the-art performance on the public KG-based fact verification dataset FactKG. |
Implicit Relation Linking for Question Answering over Knowledge Graph (2022.findings-acl)
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| Challenge: | Existing methods rely on textual similarities between NL and KG to build relation links. |
| Approach: | They propose an implicit relation linking method called ImRL which links relation phrases in NL to relation paths in KG. |
| Outcome: | The proposed method significantly outperforms state-of-the-art methods on two benchmarks and a newly-created datasets. |
Two Training Strategies for Improving Relation Extraction over Universal Graph (2021.eacl-main)
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| Challenge: | Existing methods for Distantly Supervised Relation Extraction (DS-RE) with a UG may lead to degradation in performance. |
| Approach: | They propose to use a Universal Graph (UG) to train a distantly supervised relation extraction model. |
| Outcome: | The proposed training strategies on biomedical and NYT10 datasets prove the robustness of the proposed methods and achieve state-of-the-art results. |
FRAG: A Flexible Modular Framework for Retrieval-Augmented Generation based on Knowledge Graphs (2025.findings-acl)
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| 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. |
Knowledge Graph Question Answering Leaderboard: A Community Resource to Prevent a Replication Crisis (2022.lrec-1)
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| Challenge: | Knowledge Graph (KG) Question Answering (QA) is a rapidly growing field in research and industry. |
| Approach: | They propose to create a new leaderboard for any KGQA benchmark dataset as a focal point for the community. |
| Outcome: | The proposed model provides a central and open leaderboard for any KGQA benchmark dataset as a focal point for the community. |
Thinking Beyond the Local: Multi-View Instructed Adaptive Reasoning in KG-Enhanced LLMs (2026.findings-eacl)
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| Challenge: | Existing methods for large language models adopt query-driven iterative reasoning from a local perspective, limiting efficiency and accuracy for complex multi-hop tasks. |
| Approach: | They propose a multi-view instructed adaptive reasoning of LLM on Knowledge Graphs that allows LLMs to plan, evaluate, and adapt reasoning paths from a global perspective. |
| Outcome: | The proposed model overcomes the limitations of local exploration by enabling LLMs to plan, evaluate, and adapt reasoning paths from a global perspective. |
STEM: Structure-Tracing Evidence Mining for Knowledge Graphs-Driven Retrieval-Augmented Generation (2026.acl-long)
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| Challenge: | Existing reasoning path retrieval methods lack a global structural perspective. |
| Approach: | They propose a framework that reframes multi-hop reasoning as a schema-guided graph search task. |
| Outcome: | The proposed framework improves accuracy and evidence completeness of multi-hop reasoning graph retrieval. |
Construction of Paired Knowledge Graph - Text Datasets Informed by Cyclic Evaluation (2024.lrec-main)
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Ali Mousavi, Xin Zhan, He Bai, Peng Shi, Theodoros Rekatsinas, Benjamin Han, Yunyao Li, Jeffrey Pound, Joshua M. Susskind, Natalie Schluter, Ihab F. Ilyas, Navdeep Jaitly
| Challenge: | Prior studies have shown that sequence-to-sequence models learn to hallucinate when the conditioning data has poor correlation with the sequence being produced. |
| Approach: | They construct a dataset that pairs Knowledge Graphs (KG) and text together and compare their results to a cyclic evaluation model. |
| Outcome: | The proposed model performs better on cyclic generation of KGs than on KG-T, but less well on synchronization of KTs. |
Joint Multilingual Knowledge Graph Completion and Alignment (2022.findings-emnlp)
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Vinh Tong, Dat Quoc Nguyen, Trung Thanh Huynh, Tam Thanh Nguyen, Quoc Viet Hung Nguyen, Mathias Niepert
| Challenge: | Existing work on multilingual KG completion has focused on entity and relation alignments, but understanding of how it can aid multilingual alignments is limited. |
| Approach: | They propose to combine two components that jointly accomplish KG completion and alignment. |
| Outcome: | The proposed model outperforms existing competitive baselines on a public multilingual benchmark and achieves state-of-the-art results. |
DRLK: Dynamic Hierarchical Reasoning with Language Model and Knowledge Graph for Question Answering (2022.emnlp-main)
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| Challenge: | Existing work only uses the same QA context representation to interact with multiple layers of KG, which results in a restricted interaction. |
| Approach: | They propose a model that utilizes dynamic hierarchical interactions between QA context and KG for reasoning. |
| Outcome: | The proposed model performs state-of-the-art on two benchmark datasets and competitively on the others. |
Knowledge Is Flat: A Seq2Seq Generative Framework for Various Knowledge Graph Completion (2022.coling-1)
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| 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. |
Learning Contextualized Knowledge Structures for Commonsense Reasoning (2021.findings-acl)
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Jun Yan, Mrigank Raman, Aaron Chan, Tianyu Zhang, Ryan Rossi, Handong Zhao, Sungchul Kim, Nedim Lipka, Xiang Ren
| Challenge: | Recent knowledge graph (KG) augmented models have achieved notable success on commonsense reasoning tasks. |
| Approach: | They propose a KG-augmented model that contextualizes extracted and generated knowledge by reasoning over both within a single graph structure. |
| Outcome: | The proposed model outperforms existing models on four commonsense reasoning benchmarks and a user study on edge validness and helpfulness. |
One2Set: Generating Diverse Keyphrases as a Set (2021.acl-long)
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| Challenge: | Recent keyphrase generation models are wrongly imposing a predefined order on keyphrases . a new training paradigm is proposed to concatenate keyphrase sequences in parallel . |
| Approach: | They propose a training paradigm that concatenates keyphrases in a predefined order . they propose combining a fixed set of learned control codes with a bipartite matching mechanism . |
| Outcome: | The proposed model outperforms the state-of-the-art methods on multiple benchmarks. |
How Knowledge Graph and Attention Help? A Qualitative Analysis into Bag-level Relation Extraction (2021.acl-long)
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| Challenge: | Knowledge Graph (KG) and attention mechanism have been demonstrated effective in introducing and selecting useful information for weakly supervised methods. |
| Approach: | They propose a paradigm to quantitatively evaluate the effect of attention and KG on bag-level relation extraction (RE) they propose to incorporate entity prior to KG-enhanced attention to improve RE performance . |
| Outcome: | The proposed model achieves significant improvements on two real-world datasets compared with three state-of-the-art baselines. |
Using Pairwise Occurrence Information to Improve Knowledge Graph Completion on Large-Scale Datasets (D19-1)
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| Challenge: | Existing methods for knowledge graph completion require large batch sizes and memory constraints. |
| Approach: | They combine occurrences of entity-relation pairs to construct a joint learning model using a dataset containing 2 million entities and combine them to increase the quality of sampled negatives. |
| Outcome: | The proposed model outperforms the baseline model on a dataset containing 2 million entities by 2.8% absolute on hits@1. |
Connecting the Dots: A Knowledgeable Path Generator for Commonsense Question Answering (2020.findings-emnlp)
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| 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. |
The Integration of Semantic and Structural Knowledge in Knowledge Graph Entity Typing (2024.naacl-long)
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| Challenge: | Existing methods to predict missing type annotations for knowledge graphs use only structural knowledge in the local neighborhood of entities. |
| Approach: | They propose a model for KG Entity Typing that integrates semantic and structural knowledge to infer missing types. |
| Outcome: | The proposed framework outperforms existing state-of-the-art methods in the Knowledge Graph Entity Typing task. |
Knowledge Graph-Enhanced Large Language Models via Path Selection (2024.findings-acl)
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| 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. |
Mixture-of-Partitions: Infusing Large Biomedical Knowledge Graphs into BERT (2021.emnlp-main)
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| Challenge: | Infusing factual knowledge into pre-trained models is fundamental for many knowledge-intensive tasks. |
| Approach: | They propose an infusion approach that partitions a large knowledge graph into smaller sub-graphs and infuses their specific knowledge into various BERT models using lightweight adapters. |
| Outcome: | The proposed approach improves the underlying BERTs and achieves new SOTA performance on six downstream tasks. |
KLMo: Knowledge Graph Enhanced Pretrained Language Model with Fine-Grained Relationships (2021.findings-emnlp)
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| Challenge: | Existing knowledge-enhanced pretrained language models focus on entity information and ignore fine-grained relationships between entities. |
| Approach: | They propose to incorporate KG into the language learning process to obtain a KG-enhanced pretrained Language Model. |
| Outcome: | The proposed model improves on several knowledge-driven tasks, such as entity typing and relation classification, compared with the state-of-the-art knowledge-enhanced PLMs. |
Graph Explorer: Training Faithful KG Agents with Visibility-Grounded Supervision (2026.findings-acl)
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| 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. |
Knowledge Graph Enhanced Neural Machine Translation via Multi-task Learning on Sub-entity Granularity (2020.coling-main)
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| Challenge: | Existing methods to integrate knowledge graph (KG) with neural machine translation (NMT) have two problems: knowledge under-utilization and granularity mismatch. |
| Approach: | They propose a multi-task learning method on sub-entity granularity to combine machine translation and knowledge reasoning tasks. |
| Outcome: | The proposed method significantly outperforms baseline models on translation tasks and handling the entities. |
Modeling Event-Pair Relations in External Knowledge Graphs for Script Reasoning (2021.findings-acl)
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| Challenge: | Existing methods focus on graph triples with event overlap, but ignore more supportive triples . Script reasoning relies on understanding the relationship between two events . |
| Approach: | They propose a model to learn the inferential relations between events from the whole eventuality KG . they propose 'script adapter' to extend the model to infer the associated relations between an event chain and a subsequent event candidate. |
| Outcome: | The proposed model is compared with baselines using external KG or not on a script reasoning task. |
Guiding Dialogue Agents to Complex Semantic Targets by Dynamically Completing Knowledge Graph (2023.findings-acl)
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| Challenge: | Existing knowledge graphs are incomplete in tracking complex semantic relations of the target-oriented dialogue. |
| Approach: | They combine methods of knowledge retrieval and relationship prediction to construct a context-related dynamic KG and a metric to evaluate the tracked path automatically. |
| Outcome: | The proposed method can control the agent more logically and smoothly toward the complex target. |
Rule-Aware Reinforcement Learning for Knowledge Graph Reasoning (2021.findings-acl)
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| 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. |
Improving Multi-hop Question Answering over Knowledge Graphs using Knowledge Base Embeddings (2020.acl-main)
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| Challenge: | Existing multi-hop KGQA methods impose heuristic neighborhood limits, which often make it much harder to answer the input NL question. |
| Approach: | They propose to use knowledge Graphs (KG) to answer natural language queries over the KG. |
| Outcome: | The proposed method is particularly effective in performing multi-hop KGQA over sparse KGs. |
Leveraging 3D Gaussian for Temporal Knowledge Graph Embedding (2025.findings-emnlp)
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| 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. |
Adversarial Attack against Cross-lingual Knowledge Graph Alignment (2021.emnlp-main)
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Zeru Zhang, Zijie Zhang, Yang Zhou, Lingfei Wu, Sixing Wu, Xiaoying Han, Dejing Dou, Tianshi Che, Da Yan
| Challenge: | Existing studies on cross-lingual entity alignment under adversarial attacks have not been conducted. |
| Approach: | They propose to use adversarial attack techniques to perturb cross-lingual entity alignment under adversarials. |
| Outcome: | The proposed model hides the attacked entities in dense regions in two KGs, and reduces the gradient vanishing issues in the process of adversarial attacks for further improving the attack effectiveness. |
OpticE: A Coherence Theory-Based Model for Link Prediction (2022.coling-1)
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| 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. |
Linking Surface Facts to Large-Scale Knowledge Graphs (2023.emnlp-main)
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| 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. |
Does Pre-trained Language Model Actually Infer Unseen Links in Knowledge Graph Completion? (2024.naacl-long)
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| 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. |
Dynamic Anticipation and Completion for Multi-Hop Reasoning over Sparse Knowledge Graph (2020.emnlp-main)
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| 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. |
Improving Zero-Shot Cross-lingual Transfer for Multilingual Question Answering over Knowledge Graph (2021.naacl-main)
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| Challenge: | Existing approaches to solve multilingual question answering over knowledge graph (KGQA) use of bilingual lexicon induction to map training questions into those in target language circumvents language inconsistency . |
| Approach: | They propose to use bilingual lexicon induction to map training questions in source and target languages as augmented training data to minimize syntax-disorder. |
| Outcome: | The proposed model narrows the gap in zero-shot cross-lingual transfer between source and target languages. |
CosMo: Conditional Seq2Seq-based Mixture Model for Zero-Shot Commonsense Question Answering (2020.coling-main)
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| Challenge: | Identifying the implicit causes and effects of a social context is the driving capability of commonsense reasoning. |
| Approach: | They propose a conditional seq2seq-based mixture model which generates context-dependent clauses for commonsense reasoning. |
| Outcome: | The proposed model improves on the current state-of-the-art models by +5.2% over existing models. |
KG-Agent: An Efficient Autonomous Agent Framework for Complex Reasoning over Knowledge Graph (2025.acl-long)
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| 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. |
ReKG-MCTS: Reinforcing LLM Reasoning on Knowledge Graphs via Training-Free Monte Carlo Tree Search (2025.findings-acl)
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| 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. |
Efficient and Accurate Contextual Re-Ranking for Knowledge Graph Question Answering (2024.lrec-main)
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Kexuan Sun, Nicolaas Paul Jedema, Karishma Sharma, Ruben Janssen, Jay Pujara, Pedro Szekely, Alessandro Moschitti
| Challenge: | Existing approaches to QA over textual data are based on a "retrieve-then-generate" pipeline. |
| Approach: | They propose a "triple-level" labeling strategy that infers fine-grained labels and trains a re-ranker to improve relevance of retrieved triples. |
| Outcome: | The proposed pipeline improves on prior KGQA systems by 5.56% Exact Match. |
TRACE the Evidence: Constructing Knowledge-Grounded Reasoning Chains for Retrieval-Augmented Generation (2024.findings-emnlp)
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| Challenge: | Existing retrievers are not perfect and often include irrelevant documents in the retrieved set. |
| Approach: | They propose to construct knowledge-grounded reasoning chains from retrieved documents to integrate supporting evidence into RAG models. |
| Outcome: | The proposed model achieves an average performance improvement of 14.03% on three multi-hop QA datasets. |
MusKGC: A Flexible Multi-source Knowledge Enhancement Framework for Open-World Knowledge Graph Completion (2025.emnlp-main)
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| Challenge: | Open-world knowledge graph completion (KGC) aims to infer novel facts by enriching existing graphs with external knowledge sources while maintaining semantic consistency under the open-world assumption (OWA). |
| Approach: | They propose a multi-source knowledge enhancement framework based on an open-world assumption (OWA) that integrates external knowledge sources and a new evaluation strategy to validate new facts. |
| Outcome: | The proposed model achieves SOTA performance across benchmarks and the evaluation strategy effectively assesses new facts under OWA. |
Question Answering Over Temporal Knowledge Graphs (2021.acl-long)
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| Challenge: | Temporal Knowledge Graphs (Temporal KGs) provide temporal scopes (start and end times) on each edge in the Knowledge . Lack of broad coverage datasets has been limiting progress in this area . |
| Approach: | They propose a transformer-based solution that exploits recent advances in Temporal Knowledge Graph embeddings and achieves an increase of 120% in accuracy over the next best performing method. |
| Outcome: | The proposed solution improves on the only known dataset by 340x . it increases accuracy by 120% over the baseline solution . |
SPOT: Zero-Shot Semantic Parsing Over Property Graphs (2025.findings-acl)
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| 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. |
Structure-aware Knowledge Graph-to-text Generation with Planning Selection and Similarity Distinction (2023.emnlp-main)
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| Challenge: | Existing methods to generate knowledge graph-to-text (KG-to) text rely on pre-trained language models to bridge the gap between the different structures of the input KG and the target text. |
| Approach: | They propose a method that integrates graph structure-aware modules with pre-trained language models to capture the intricate topology information present in the KG. |
| Outcome: | The proposed model captures the topology information present in the knowledge graph and distinguishes similar input KGs through contrastive learning techniques. |
RHGN: Relation-gated Heterogeneous Graph Network for Entity Alignment in Knowledge Graphs (2023.findings-acl)
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| Challenge: | Existing methods for entity alignment fail to account for heterogeneity among KGs and distinction between KG entities and relations. |
| Approach: | They propose a Relation-gated Heterogeneous Graph Network (RHGN) that uses a relation-gate based convolutional layer to distinguish relations and entities in the KG. |
| Outcome: | Extensive experiments on four datasets show that the proposed method is superior to state-of-the-art methods. |
Connecting Embeddings for Knowledge Graph Entity Typing (2020.acl-main)
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| Challenge: | Existing knowledge graphs suffer from incompleteness and miss important facts, jeopardizing their usefulness in downstream tasks such as question answering. |
| Approach: | They propose a method which is trained by utilizing local typing knowledge from existing entity type assertions and global triple knowledge in KGs. |
| Outcome: | The proposed model favors inferences that agree with both entity type instances and triple knowledge in KGs. |
GLTW: Joint Improved Graph Transformer and LLM via Three-Word Language for Knowledge Graph Completion (2025.findings-acl)
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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
| 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. |
Generating Domain-Specific Knowledge Graphs from Large Language Models (2025.findings-acl)
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| 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. |
KG-TRICK: Unifying Textual and Relational Information Completion of Knowledge for Multilingual Knowledge Graphs (2025.coling-main)
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Zelin Zhou, Simone Conia, Daniel Lee, Min Li, Shenglei Huang, Umar Farooq Minhas, Saloni Potdar, Henry Xiao, Yunyao Li
| Challenge: | Existing studies have shown that combining information from KGs in different languages aids knowledge Graph Completion and Knowledge Graph Enhancement. |
| Approach: | They propose a sequence-to-sequence framework that unifies tasks of textual and relational information completion for multilingual knowledge graphs. |
| Outcome: | The proposed framework unifies tasks of KGC and KGE into a single framework. |
Time-dependent Entity Embedding is not All You Need: A Re-evaluation of Temporal Knowledge Graph Completion Models under a Unified Framework (2021.emnlp-main)
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| Challenge: | Various temporal knowledge graph (KG) completion models have been proposed . knowledge graphs are typically static and store facts in their current state . |
| Approach: | They propose to use temporal embeddings and a score function to model temporal knowledge graphs . they classify the temporal embedded methods into two classes: timestamp and time-dependent . |
| Outcome: | The proposed models outperform current models on ICEWS datasets with 3000 experiments and 13159 GPU hours. |
Concept2Box: Joint Geometric Embeddings for Learning Two-View Knowledge Graphs (2023.findings-acl)
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Zijie Huang, Daheng Wang, Binxuan Huang, Chenwei Zhang, Jingbo Shang, Yan Liang, Zhengyang Wang, Xian Li, Christos Faloutsos, Yizhou Sun, Wei Wang
| Challenge: | Existing methods to embed knowledge graphs have ignored the fact that they contain two fundamentally different views: high-level ontology-view concepts and fine-grained instance-view entities. |
| Approach: | They propose a novel geometric representation that jointly embeds the two views of a KG using dual geometric representations. |
| Outcome: | Experiments on the public DBpedia KG and a newly-created industrial KG show the proposed method works well. |
Empowering Language Models with Knowledge Graph Reasoning for Open-Domain Question Answering (2022.emnlp-main)
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| Challenge: | Existing Language Models lack the power to store all required knowledge, resulting in a lack of ability to infer out-of-context knowledge. |
| Approach: | They propose a Knowledge Interaction Layer that can be flexibly plugged into existing Transformer-based LMs to interact with a differentiable Knowledge Graph Reasoning module collaboratively. |
| Outcome: | The proposed model can be plugged into existing Transformer-based LMs to interact with a differentiable Knowledge Graph Reasoning module collaboratively. |
Developing PUGG for Polish: A Modern Approach to KBQA, MRC, and IR Dataset Construction (2024.findings-acl)
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Albert Sawczyn, Katsiaryna Viarenich, Konrad Wojtasik, Aleksandra Domogała, Marcin Oleksy, Maciej Piasecki, Tomasz Kajdanowicz
| Challenge: | Existing KBQA datasets are outdated and inefficient in human labor, and assisting tools like Large Language Models (LLM) are not utilized to reduce the workload. |
| Approach: | They propose a semi-automated question answering task that uses structured knowledge graphs to answer extensive knowledge-intensive questions. |
| Outcome: | The proposed approach includes KBQA, MRC, and Information Retrieval tasks for low-resource languages. |
Learning Neural Ordinary Equations for Forecasting Future Links on Temporal Knowledge Graphs (2021.emnlp-main)
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| Challenge: | Existing models for temporal knowledge graphs model the temporal KGs in discrete state spaces, whereas static models model the KG in discretized state spaces. |
| Approach: | They propose a continuum model that extends the idea of neural ordinary differential equations to multi-relational graph convolutional networks and encodes both temporal and structural information into continuous-time dynamic embeddings. |
| Outcome: | The proposed model outperforms existing models on five benchmark datasets showing it can predict future links on temporal knowledge graphs. |
Tree-of-Traversals: A Zero-Shot Reasoning Algorithm for Augmenting Black-box Language Models with Knowledge Graphs (2024.acl-long)
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Elan Markowitz, Anil Ramakrishna, Jwala Dhamala, Ninareh Mehrabi, Charith Peris, Rahul Gupta, Kai-Wei Chang, Aram Galstyan
| 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. |
A Framework of Knowledge Graph-Enhanced Large Language Model Based on Question Decomposition and Atomic Retrieval (2024.findings-emnlp)
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| 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. |
Knowledge Graph - Deep Learning: A Case Study in Question Answering in Aviation Safety Domain (2022.lrec-1)
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Ankush Agarwal, Raj Gite, Shreya Laddha, Pushpak Bhattacharyya, Satyanarayan Kar, Asif Ekbal, Prabhjit Thind, Rajesh Zele, Ravi Shankar
| Challenge: | Existing Question Answering systems for commercial aviation use a large number of documents . a Knowledge Graph (KG) guided Deep Learning (DL) based system can be used to query the documents based on accident reports . |
| Approach: | They propose a Knowledge Graph (KG) guided Deep Learning (DL) based Question Answering system to cater to these requirements. |
| Outcome: | The proposed system achieves 7% and 40% increase in accuracy over existing systems. |
RELATE: Generating a linguistically inspired Knowledge Graph for fine-grained emotion classification (2022.lrec-1)
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| Challenge: | Existing knowledge resources for sentiment analysis (SA) tasks are either large, common-sense knowledge graphs (KGs) that cover a limited amount of polarities/emotions or they are smaller in size (e.g. lexicons) . however, these resources are limited by the low coverage of e.t. and scalability. |
| Approach: | They propose a new directed KG called ‘RELATE’ which incorporates the benefit of semantics without relying on costly human annotation. |
| Outcome: | The proposed KG overcomes low coverage of emotions and scalability issues . it is the first KG of its size to cover Ekman’s six basic emotions that are directed towards entities. |
Learning Collaborative Agents with Rule Guidance for Knowledge Graph Reasoning (2020.emnlp-main)
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| Challenge: | Walk-based models have shown their advantages in knowledge graph reasoning but are limited by their representations and generalizability. |
| Approach: | They propose a walk-based model that leverages high-quality rules generated by symbolic-based methods to provide reward supervision for walk- based agents. |
| Outcome: | Experiments on benchmark datasets show that RuleGuider improves the performance of walk-based models without losing interpretability. |
Knowledge-guided Open Attribute Value Extraction with Reinforcement Learning (2020.emnlp-main)
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| Challenge: | Existing methods for open attribute value extraction for emerging entities are noisy or incomplete, even missing. |
| Approach: | They propose a knowledge-guided reinforcement learning framework for open attribute value extraction for emerging entities. |
| Outcome: | The proposed framework outperforms baselines by 16.5 - 27.8%. |
Text Augmented Open Knowledge Graph Completion via Pre-Trained Language Models (2023.findings-acl)
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| Challenge: | Existing methods to augment knowledge graph completion require factual triples or manual prompts to extract knowledge from a pre-trained language model. |
| Approach: | They propose a tool that generates quality query prompts and retrieves support information from large text corpora to probe knowledge from a pre-trained language model. |
| Outcome: | The proposed method outperforms embedding-based, graph-based and PLM-based methods on two benchmark datasets. |
A Collaborative Reasoning Framework Powered by Reinforcement Learning and Large Language Models for Complex Questions Answering over Knowledge Graph (2025.coling-main)
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| 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. |
A Benchmark for Semi-Inductive Link Prediction in Knowledge Graphs (2023.findings-emnlp)
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| Challenge: | Semi-inductive link prediction (LP) is a task of predicting facts for new, previously unseen entities based on context information. |
| Approach: | They propose to use Wikidata5M to evaluate semi-inductive link prediction (LP) in knowledge graphs. |
| Outcome: | The proposed benchmark provides a test bed for further research into semi-inductive link prediction (LP) in knowledge graphs. |
HOLMES: Hyper-Relational Knowledge Graphs for Multi-hop Question Answering using LLMs (2024.acl-long)
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| Challenge: | Existing approaches to answer multi-hop questions are query-agnostic and the extracted facts are ambiguous as they lack context. |
| Approach: | They propose to use a knowledge graph to extract query-relevant information from unstructured text. |
| Outcome: | The proposed method achieves performance improvements on two popular datasets. |
SciNLP: A Domain-Specific Benchmark for Full-Text Scientific Entity and Relation Extraction in NLP (2025.emnlp-main)
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| Challenge: | Existing datasets for structured information extraction focus on specific publication sections due to domain complexity and high cost of annotating scientific texts. |
| Approach: | They propose a specialized benchmark for full-text entity and relation extraction in the natural language processing domain. |
| Outcome: | The proposed dataset comprises 60 manually annotated full-text NLP publications covering 7,072 entities and 1,826 relations. |
How Sememic Components Can Benefit Link Prediction for Lexico-Semantic Knowledge Graphs? (2025.emnlp-main)
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| Challenge: | Existing methods to predict missing triples in Knowledge Graphs are limited by semantic information. |
| Approach: | They propose a method to leverage sememe knowledge to enhance LP . LP is a technique that integrates structural and textual information into a Knowledge Graph . |
| Outcome: | The proposed method improves LP performance in English and Chinese . it improves on WN18RR, HN7 and CWN5, respectively . |
Knowledge Graph Representation Learning using Ordinary Differential Equations (2021.emnlp-main)
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| Challenge: | Knowledge Graph Embeddings (KGEs) map entities and relations from knowledge graphs into a geometric space. |
| Approach: | They propose a neuro differential KGE that embeds nodes of a KG on the trajectories of Ordinary Differential Equations (ODEs) they represent each relation (edge) in a knowledge graph as a vector field on several manifolds. |
| Outcome: | The proposed model can preserve graph characteristics including structural aspects and semantics and avoid wrong inferences. |
IDEATE: Detecting AI-Generated Text Using Internal and External Factual Structures (2024.lrec-main)
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| Challenge: | Existing methods to detect AI-generated text rely on internal evidences, but external evidences are not considered. |
| Approach: | They propose a hierarchical graph network that utilizes internal and external factual structures to detect AI-generated text. |
| Outcome: | The proposed network outperforms current state-of-the-art methods on four datasets. |
Text-Attributed Knowledge Graph Enrichment with Large Language Models for Medical Concept Representation (2026.acl-long)
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| Challenge: | eHRs encode a patient's medical history as a high-dimensional and sparse sequence of diagnosis, medication, and procedure concepts . robust concept representation learning is hindered by key challenges, authors say . clinically important cross-type dependencies are often missing or incomplete in existing ontology resources . |
| Approach: | They propose a graph learning framework that integrates semantics with medical concepts to improve prediction performance. |
| Outcome: | The proposed framework improves prediction performance and integrates semantics with graph structure. |
Query2Triple: Unified Query Encoding for Answering Diverse Complex Queries over Knowledge Graphs (2023.findings-emnlp)
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| 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. |
ClaimVer: Explainable Claim-Level Verification and Evidence Attribution of Text Through Knowledge Graphs (2024.findings-emnlp)
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Preetam Prabhu Srikar Dammu, Himanshu Naidu, Mouly Dewan, YoungMin Kim, Tanya Roosta, Aman Chadha, Chirag Shah
| Challenge: | Despite the fact that many fact-checking tools lack granularity and explainability, they lack the ability to be useful in various contexts. |
| Approach: | They propose a text validation framework that provides granular explanations for each claim and localizes the specific problematic content to reduce cognitive load. |
| Outcome: | The proposed framework provides granular explanations for each claim prediction and localizes and educates users on the specific content. |
Joint Completion and Alignment of Multilingual Knowledge Graphs (2022.emnlp-main)
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| Challenge: | Existing methods for knowledge graph completion are incomplete, as curators struggle to keep up with the real world. |
| Approach: | They propose a multitask approach to solve missing facts in incomplete Knowledge Graphs . they add a relation representation to the existing KG embedding scheme . |
| Outcome: | The proposed system outperforms existing models in seven languages compared to existing models . it also outperformed existing models, underscoring the value of joint alignment and completion. |
Tox-BART: Leveraging Toxicity Attributes for Explanation Generation of Implicit Hate Speech (2024.findings-acl)
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| Challenge: | Existing language models to generate implicit hate explanations are lacking in many fields. |
| Approach: | They propose to use language models to generate explicit hate posts to make it clear . they find that simpler models incorporating external toxicity signals outperform KG-infused models . |
| Outcome: | The proposed setup produces more precise explanations than zero-shot GPT-3.5, highlighting the intricate nature of the task. |
Reliable and Cost-Effective Exploratory Data Analysis via Graph-Guided RAG (2025.emnlp-main)
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Mossad Helali, Yutai Luo, Tae Jun Ham, Jim Plotts, Ashwin Chaugule, Jichuan Chang, Parthasarathy Ranganathan, Essam Mansour
| Challenge: | Large Language Models (LLMs) have limited accuracy and code reliability on less-studied or private datasets. |
| Approach: | They propose a graph-guided framework that extracts EDA operation semantics from a knowledge graph and then generates executable Python code. |
| Outcome: | Experiments on two datasets show that RAGvis significantly improves code executability, semantic accuracy, and visual quality compared to LLM-only baselines. |
Double-Branch Multi-Attention based Graph Neural Network for Knowledge Graph Completion (2023.acl-long)
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| Challenge: | Existing knowledge graph embedding methods cannot capture local and global information and are not designed well to learn representations of seen entities with sparse neighborhoods in isolated subgraphs. |
| Approach: | They propose a double-branch multi-attention based graph neural network to learn more expressive entity representations which contain rich global-local structural information. |
| Outcome: | The proposed method outperforms a general GNN-based approach for KGC. |
GNN-RAG: Graph Neural Retrieval for Efficient Large Language Model Reasoning on Knowledge Graphs (2025.findings-acl)
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| Challenge: | Existing approaches to retrieval-augmented generation (RAG) rely on costly LLM calls to generate relation paths or traverse the KG. |
| Approach: | They propose a framework that uses lightweight Graph Neural Networks to enhance retrieval. |
| Outcome: | The proposed framework outperforms existing methods on multi-hop and multi-entity questions. |
KPatch: Knowledge Patch to Pre-trained Language Model for Zero-Shot Stance Detection on Social Media (2024.lrec-main)
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Shuohao Lin, Wei Chen, Yunpeng Gao, Zhishu Jiang, Mengqi Liao, Zhiyu Zhang, Shuyuan Zhao, Huaiyu Wan
| Challenge: | Existing knowledge injection methods fail to understand the semantics of tweets . |
| Approach: | They propose a method to flexibly inject knowledge into a pre-trained language model and adaptively expand tweets context. |
| Outcome: | The proposed method is based on two training stages to flexibly inject knowledge into the pre-trained language model and adaptively expand tweets context. |
SAFE: Schema-Driven Approximate Distance Join for Efficient Knowledge Graph Querying (2025.emnlp-main)
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| 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. |
Systematic Assessment of Factual Knowledge in Large Language Models (2023.findings-emnlp)
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| 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. |
DrKGC: Dynamic Subgraph Retrieval-Augmented LLMs for Knowledge Graph Completion across General and Biomedical Domains (2025.findings-emnlp)
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| 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. |
FactKG: Fact Verification via Reasoning on Knowledge Graphs (2023.acl-long)
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| 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 . |
Relation-Aware Question Answering for Heterogeneous Knowledge Graphs (2023.findings-emnlp)
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| 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. |
Multilingual Generation and Answering of Questions from Texts and Knowledge Graphs (2023.findings-emnlp)
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| Challenge: | Existing methods for QG-QA are limited to English, but can be used in other languages. |
| Approach: | They propose to bring multilinguality to multimodal QG-QA by using Brazilian Portuguese and Russian data. |
| Outcome: | The proposed approach outperforms a baseline on English and can handle both languages. |
KERL: Knowledge-Enhanced Personalized Recipe Recommendation using Large Language Models (2025.acl-long)
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| Challenge: | Recent advances in large language models and the abundance of food data have led to studies to improve food understanding using LLMs. |
| Approach: | They propose a unified system that leverages food KGs and LLMs to provide personalized food recommendations and generate recipes with associated micro-nutritional information. |
| Outcome: | The proposed system outperforms existing methods in food recommendation, recipe generation and nutritional analysis. |
Generate-on-Graph: Treat LLM as both Agent and KG for Incomplete Knowledge Graph Question Answering (2024.emnlp-main)
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Yao Xu, Shizhu He, Jiabei Chen, Zihao Wang, Yangqiu Song, Hanghang Tong, Guang Liu, Jun Zhao, Kang Liu
| 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. |
Beyond Completion: A Foundation Model for General Knowledge Graph Reasoning (2025.findings-acl)
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Yin Hua, Zhiqiang Liu, Mingyang Chen, Zheng Fang, Chi Man Wong, Lingxiao Li, Chi Man Vong, Huajun Chen, Wen Zhang
| Challenge: | Existing foundation models for general knowledge graph reasoning have focused on their structural aspects, with most efforts restricted to in-KG tasks. |
| Approach: | They propose a conditional encoding architecture that bridges the gap between textual and structural modalities, enabling seamless integration. |
| Outcome: | The proposed model outperforms baseline models on 28 datasets and is generalized to out-of-KG tasks. |
VistaWise: Building Cost-Effective Agent with Cross-Modal Knowledge Graph for Minecraft (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) have shown significant promise in embodied decision-making tasks within virtual open-world environments, but lack domain-specific knowledge. |
| Approach: | They propose a cost-effective agent framework that integrates cross-modal domain knowledge and finetunes a dedicated object detection model for visual analysis. |
| Outcome: | The proposed framework reduces the requirement for domain-specific training data from millions of samples to a few hundred. |
Prior Relational Schema Assists Effective Contrastive Learning for Inductive Knowledge Graph Completion (2024.lrec-main)
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| Challenge: | Existing knowledge graphs lack robustness and incompleteness to provide link prediction. |
| Approach: | They propose to capture prior schema-level interactions related to relations by leveraging entity type information and introduce schema-guided negatives to bolster the efficiency of normal contrastive representation learning. |
| Outcome: | The proposed method achieves state-of-the-art performance on multiple established metrics across multiple datasets for link prediction. |
Prompt-based Generation of Natural Language Explanations of Synthetic Lethality for Cancer Drug Discovery (2024.lrec-main)
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| Challenge: | Synthetic lethality (SL) is a genetic interaction where a single gene mutation allows cell survival, but simultaneous mutations in two genes lead to cell death. |
| Approach: | They propose a prompt-based pipeline for generating natural language explanations using a dataset derived from New Bing . |
| Outcome: | The proposed pipeline improves on existing biomedical language models in terms of text quality and explainability. |
Prompt-fused Framework for Inductive Logical Query Answering (2024.lrec-main)
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| 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. |
Just KIDDIN’ : Knowledge Infusion and Distillation for Detection of INdecent Memes (2025.findings-acl)
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| 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. |
Decoding on Graphs: Faithful and Sound Reasoning on Knowledge Graphs through Generation of Well-Formed Chains (2025.acl-long)
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| 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. |
Retrieval-based Question Answering with Passage Expansion Using a Knowledge Graph (2024.lrec-main)
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| Challenge: | Recent advances in dense neural retrievers and language models have hindered performance, especially for less common entities and facts. |
| Approach: | They propose a multi-modal passage retrieval model that combines entity features and textual data to improve retrieval precision for less common entities. |
| Outcome: | The proposed model improves retrieval precision on less common entities and facts on common benchmarks. |
Large Language Models Meet Knowledge Graphs for Question Answering: Synthesis and Opportunities (2025.emnlp-main)
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| 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. |
Knowledge Graph Retrieval-Augmented Generation for LLM-based Recommendation (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) have produced significant advances in the field of recommender systems. |
| Approach: | They propose to retrieve up-to-date structure information from the knowledge graph to augment recommendations by leveraging external knowledge sources. |
| Outcome: | Experiments on a large dataset show that the proposed method is effective in enhancing LLM-based recommendations. |
LLMs as Knowledge Graph Refiners: Mitigating Factual Inconsistencies in Generative Knowledge Extraction (2026.acl-long)
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| 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. |
BYOKG-RAG: Multi-Strategy Graph Retrieval for Knowledge Graph Question Answering (2025.emnlp-main)
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Costas Mavromatis, Soji Adeshina, Vassilis N. Ioannidis, Zhen Han, Qi Zhu, Ian Robinson, Bryan Thompson, Huzefa Rangwala, George Karypis
| Challenge: | Existing approaches to knowledge graph question answering (KGQA) rely on Large Language Model (LLM) agents for graph traversal and retrieval. |
| Approach: | They propose a framework that synergizes Large Language Models with specialized graph retrieval tools to enhance KGQA. |
| Outcome: | The proposed framework outperforms the second-best graph retrieval method by 4.5% points while showing better generalization to custom KGs. |
G-HiRel: Enhancing the Adaption to Knowledge Updating for Large Language Model Reasoning (2026.findings-acl)
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| 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. |
Thought-Action Graph Reasoning: Faithful and Efficient Reasoning of Large Language Models via Reusing Past Experience (2026.findings-acl)
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Zhixiao Qi, Feng Huang, Yunqi Zhang, Shijie Zhang, Qingqing Sun, Yongfeng Huang, Minghu Jiang, Shuai Chen, Tianyi Zhang
| 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. |
CAMEC: Complexity-Aware Multi-Expert Collaboration for Reliable Chinese Medical Question Answering (2026.acl-long)
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| Challenge: | Large language models are promising for medical question answering in china, but remain unreliable due to hallucinations, weak factual grounding and difficulty handling clinically complex cases. |
| Approach: | They propose a framework that combines hierarchical medical adaptation with complexity-aware expert routing for reliable Chinese medical QA. |
| Outcome: | The proposed framework outperforms strong general and medical LLM baselines on four Chinese medical benchmarks. |