KGC

SpeedE: Euclidean Geometric Knowledge Graph Embedding Strikes Back (2024.findings-naacl)

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

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Aleksandar Pavlović, Emanuel Sallinger

Bilateral Masking with prompt for Knowledge Graph Completion (2024.findings-naacl)

Challenge:	Geometric knowledge graph embedding models (gKGEs) have shown great potential for knowledge graph completion (KGC) however, contemporary gKges require high embeddable dimensionalities or complex embeddances for good KGC performance, drastically limiting their time and space efficiency.
Approach:	They propose a lightweight Euclidean gKGE that provides strong inference capabilities and significantly outperforms state-of-the-art gGKGEs.
Outcome:	The proposed model outperforms state-of-the-art gKGEs on YAGO3-10 and WN18RR while significantly increasing their efficiency.

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Yonghui Kong, Cunhang Fan, Yujie Chen, Shuai Zhang, Zhao Lv, Jianhua Tao

DynaSemble: Dynamic Ensembling of Textual and Structure-Based Models for Knowledge Graph Completion (2024.acl-short)

Challenge:	Existing word matching methods fail to obtain satisfactory single embedding representations for entities.
Approach:	They propose a bi-encoder-based approach to enhance entity representations by using prompts to narrow the distance between the predicted entity and the known entity.
Outcome:	The proposed model achieves state-of-the-art performance on the WN18RR dataset.

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Ananjan Nandi, Navdeep Kaur, Parag Singla, Mausam .

Contextualization Distillation from Large Language Model for Knowledge Graph Completion (2024.findings-eacl)

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.

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Dawei Li, Zhen Tan, Tianlong Chen, Huan Liu

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

Challenge:	Existing knowledge graph completion models lack textual information, which limits their performance . a plug-in-and-play approach is needed to train small models in descriptive context .
Approach:	They propose a plug-in-and-play approach to knowledge graph completion that prompts LLMs to generate descriptive context.
Outcome:	The proposed method improves performance on Wikipedia articles and synset definitions.

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

COSIGN: Contextual Facts Guided Generation for Knowledge Graph Completion (2024.naacl-long)

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

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Jinpeng Li, Hang Yu, Xiangfeng Luo, Qian Liu

There Is No Standard Answer: Knowledge-Grounded Dialogue Generation with Adversarial Activated Multi-Reference Learning (2022.emnlp-main)

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.

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Xueliang Zhao, Tingchen Fu, Chongyang Tao, Rui Yan

Dialogue Systems & Conversational Agents

Challenge:	Existing methods emphasize selecting one golden knowledge given a particular dialogue context, overlooking the one-to-many phenomenon in dialogue.
Approach:	They propose to use a multi-reference dataset to assess the one-to-many efficacy of existing KGC models.
Outcome:	The proposed model improves the mapping relationship between multiple knowledge and multiple responses by optimizing the model in a wake-sleep style.

ACTC: Active Threshold Calibration for Cold-Start Knowledge Graph Completion (2023.acl-short)

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Anastasiia Sedova, Benjamin Roth

Decoupling Mixture-of-Graphs: Unseen Relational Learning for Knowledge Graph Completion by Fusing Ontology and Textual Experts (2022.coling-1)

Challenge:	Knowledge graphs are a graph of information organized as entities, relations, and entities.
Approach:	They propose a method to calibrate a scoring model over (entity, relation, entity)-tuples . they use an annotated set of tuple truncated by Logistic Regression or Gaussian Process classifiers .
Outcome:	The proposed method finds good per-relation thresholds efficiently based on a limited set of annotated tuples.

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Ran Song, Shizhu He, Suncong Zheng, Shengxiang Gao, Kang Liu, Zhengtao Yu, Jun Zhao

CAKE: A Scalable Commonsense-Aware Framework For Multi-View Knowledge Graph Completion (2022.acl-long)

Challenge:	Existing methods for knowledge Graph Completion (KGC) fail in unseen relation representations.
Approach:	They propose to use three kinds of graphs to obtain unseen relation representations . they propose to decouple mixture-of-graph experts (DMoG) for unseened relations learning .
Outcome:	The proposed method outperforms the state-of-the-art methods on unseen relation representations.

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Guanglin Niu, Bo Li, Yongfei Zhang, Shiliang Pu

Retrieval, Reasoning, Re-ranking: A Context-Enriched Framework for Knowledge Graph Completion (2025.naacl-long)

Challenge:	Existing knowledge graph embedding techniques rely on fact-view data to predict missing links between entities, limiting their performance.
Approach:	They propose a commonsense-aware knowledge embedding framework which generates commonsensense from factual triples with entity concepts for a KGC task.
Outcome:	The proposed framework could produce high-quality negative triples and joint commonsense and fact-view link prediction.

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Muzhi Li, Cehao Yang, Chengjin Xu, Xuhui Jiang, Yiyan Qi, Jian Guo, Ho-fung Leung, Irwin King

Path-enhanced Pre-trained Language Model for Knowledge Graph Completion (2025.findings-emnlp)

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.

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Hao Wang, Dandan Song, Zhijing Wu, Yuhang Tian, Pan Yang

There Are a Thousand Hamlets in a Thousand People’s Eyes: Enhancing Knowledge-grounded Dialogue with Personal Memory (2022.acl-long)

Challenge:	Pre-trained language models have achieved remarkable knowledge graph completion (KGC) success.
Approach:	They propose a path-enhanced pre-trained language model-based knowledge graph completion method which uses multi-view generation to infer missing facts in triple-level and path-level simultaneously.
Outcome:	The proposed method significantly improves the performance of the knowledge graph completion task.

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Tingchen Fu, Xueliang Zhao, Chongyang Tao, Ji-Rong Wen, Rui Yan

Dialogue Systems & Conversational Agents

Challenge:	Existing methods for knowledge selection focus on relevance between knowledge and dialogue context, ignoring personal preference for knowledge.
Approach:	They propose to introduce personal memory into knowledge selection in chatbots to address personalization issue by integrating personal memory and inverse mapping into a closed loop.
Outcome:	The proposed method outperforms existing methods significantly on automatic evaluation and human evaluation.

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

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Ishaan Singh, Navdeep Kaur, Garima Gaur, null Mausam

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

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

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

SimKGC: Simple Contrastive Knowledge Graph Completion with Pre-trained Language Models (2022.acl-long)

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

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Liang Wang, Wei Zhao, Zhuoyu Wei, Jingming Liu

To Copy Rather Than Memorize: A Vertical Learning Paradigm for Knowledge Graph Completion (2023.acl-long)

Challenge:	Text-based methods lag behind graph embedding-based approaches for knowledge graph completion (KGC)
Approach:	They propose three types of negatives to improve contrastive learning to improve learning efficiency.
Outcome:	The proposed model outperforms embedding-based methods on several benchmark datasets.

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Rui Li, Xu Chen, Chaozhuo Li, Yanming Shen, Jianan Zhao, Yujing Wang, Weihao Han, Hao Sun, Weiwei Deng, Qi Zhang, Xing Xie

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

Challenge:	Existing methods for embedding knowledge graphs implicitly memorize relation rules to infer missing links, but they are difficult to memorize due to the inherent deficiencies of such implicit memorization strategy.
Approach:	They propose a vertical learning paradigm that allows to explicitly copy target information from related factual triples for more accurate prediction.
Outcome:	The proposed model improves generalization ability and makes distant link prediction significantly easier.

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Alla Chepurova, Aydar Bulatov, Yuri Kuratov, Mikhail Burtsev

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

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

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

Improving Knowledge Graph Completion with Generative Hard Negative Mining (2023.findings-acl)

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

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Zile Qiao, Wei Ye, Dingyao Yu, Tong Mo, Weiping Li, Shikun Zhang

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

Challenge:	Existing methods for knowledge graph completion (KGC) use generative methods with a self-information-enhanced training strategy to generate high-quality negatives.
Approach:	They propose to leverage a sequence-to-sequence architecture to generate high-quality hard negatives from the same decoding distributions as the anchor.
Outcome:	The proposed method produces high-quality negatives with good hardness and diversity on three KGC benchmarks.

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Linhai Zhang, Deyu Zhou

Schema-adaptable Knowledge Graph Construction (2023.findings-emnlp)

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

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Hongbin Ye, Honghao Gui, Xin Xu, Xi Chen, Huajun Chen, Ningyu Zhang

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

Challenge:	Existing Knowledge Graph Construction (KGC) tasks rely on static information extraction with a closed set of pre-defined schemas.
Approach:	They propose a static knowledge Graph Construction task that extracts entity, relation, and event based on dynamically changing schema graph without retraining.
Outcome:	The proposed system outperforms existing methods but still has room for improvement . it can extract entity, relation, and event based on dynamically changing schema graph without re-training .

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Yusuke Sakai, Hidetaka Kamigaito, Katsuhiko Hayashi, Taro Watanabe

Advancing Cross-Lingual Entity Alignment with Large Language Models: Tailored Sample Segmentation and Zero-Shot Prompts (2024.findings-emnlp)

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

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Linyan Yang, Jingwei Cheng, Fu Zhang

A Re-evaluation of Knowledge Graph Completion Methods (2020.acl-main)

Challenge:	Existing approaches to integrate large language models into cross-lingual entity alignment tasks pose challenges in handling large-scale data, generating suitable data samples, and adapting prompts for the EA task.
Approach:	They propose a framework that integrates distance feature extraction, sample Segmentation, and zero-shot prompts to integrate LLMs into cross-lingual entity alignment tasks.
Outcome:	The proposed framework is able to extract features from large-scale data and adapt prompts to the task.

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Zhiqing Sun, Shikhar Vashishth, Soumya Sanyal, Partha Talukdar, Yiming Yang

Data Collection vs. Knowledge Graph Completion: What is Needed to Improve Coverage? (2021.emnlp-main)

Challenge:	Knowledge Graph Completion (KGC) aims at automatically predicting missing links for large-scale knowledge graphs.
Approach:	They propose a protocol to evaluate KGC methods that is robust to handle bias in the model, which can substantially affect the final results.
Outcome:	The proposed evaluation protocol is robust to handle bias in the model, which can substantially affect the final results.

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Kenneth Church, Yuchen Bian

MusKGC: A Flexible Multi-source Knowledge Enhancement Framework for Open-World Knowledge Graph Completion (2025.emnlp-main)

Challenge:	Knowledge Graph Completion (KGC) attempts to learn missing links from subsets.
Approach:	This survey/position paper discusses ways to improve coverage of resources such as WordNet.
Outcome:	The proposed method improves WordNet coverage by reducing the number of words in the sample and reducing unbalanced corpora.

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Xin Song, Liu Haiyan, Haiyang Wang, Ye Wang, Kai Chen, Bin Zhou

Knowledge Context Modeling with Pre-trained Language Models for Contrastive Knowledge Graph Completion (2024.findings-acl)

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.

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Guangqian Yang, Yi Liu, Lei Zhang, Licheng Zhang, Hongtao Xie, Zhendong Mao

Are Missing Links Predictable? An Inferential Benchmark for Knowledge Graph Completion (2021.acl-long)

Challenge:	Text-based knowledge graph completion methods neglect knowledge contexts in inferring process.
Approach:	They propose a framework which models the knowledge context as additional prompts with pre-trained language models for knowledge graph completion.
Outcome:	The proposed framework achieves state-of-the-art on FB15k-237, WN18RR and Wikidata5M datasets.

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Yixin Cao, Xiang Ji, Xin Lv, Juanzi Li, Yonggang Wen, Hanwang Zhang

Extract, Define, Canonicalize: An LLM-based Framework for Knowledge Graph Construction (2024.emnlp-main)

Challenge:	Existing benchmarks for Knowledge Graph Completion (KGC) are unsatisfactory .
Approach:	They propose to use rule-guided train/test generation instead of conventional random split to ensure that each testing sample is predictable with supportive data in the training set.
Outcome:	The proposed model improves on existing benchmarks in inferential ability, assumptions, and patterns.

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Bowen Zhang, Harold Soh

KICGPT: Large Language Model with Knowledge in Context for Knowledge Graph Completion (2023.findings-emnlp)

Challenge:	Existing methods for knowledge graph creation (KGC) are limited in their ability to scale up to text common in many real-world applications.
Approach:	They propose a framework for knowledge graph creation from input text using a pre-defined schema and a trained component that retrieves schema elements relevant to the input text.
Outcome:	The proposed framework extract-define-canonicalize extracts high-quality triplets with a succinct self-generated schema without any parameter tuning and with significantly larger schemas compared to prior works.

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Yanbin Wei, Qiushi Huang, Yu Zhang, James Kwok

Multilingual Knowledge Graph Completion with Language-Sensitive Multi-Graph Attention (2023.acl-long)

Challenge:	Existing knowledge graph completion methods struggle with long-tail entities due to limited structural information and imbalanced distributions of entities.
Approach:	They propose a framework that integrates a large language model and a triple-based KGC retriever to alleviate the long-tail problem without incurring additional training overhead.
Outcome:	The proposed model reduces training overhead and finetuning costs on benchmark datasets.

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Rongchuan Tang, Yang Zhao, Chengqing Zong, Yu Zhou

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

Challenge:	Existing approaches to multilingual knowledge graph completion have two drawbacks: alignment dependency and training inefficiency.
Approach:	They propose a multilingual knowledge graph completion framework with language-sensitive multi-graph attention to predict missing links on all given KGs.
Outcome:	The proposed model improves on the DBP-5L and E-PKG datasets.

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

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

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

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

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

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

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

KG-TRICK: Unifying Textual and Relational Information Completion of Knowledge for Multilingual Knowledge Graphs (2025.coling-main)

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

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Zelin Zhou, Simone Conia, Daniel Lee, Min Li, Shenglei Huang, Umar Farooq Minhas, Saloni Potdar, Henry Xiao, Yunyao Li

Generating and Evaluating Plausible Explanations for Knowledge Graph Completion (2024.acl-long)

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.

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Antonio Di Mauro, Zhao Xu, Wiem Ben Rim, Timo Sztyler, Carolin Lawrence

Dipping PLMs Sauce: Bridging Structure and Text for Effective Knowledge Graph Completion via Conditional Soft Prompting (2023.findings-acl)

Challenge:	Existing XAI approaches focus on learning algorithmic explanations, but are not plausible for users.
Approach:	They propose a path-based explanation method that meets human-centric explainability constraints and enhances plausibility.
Outcome:	The proposed method meets human-centric explainability constraints and enhances plausibility.

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

Filter-then-Generate: Large Language Models with Structure-Text Adapter for Knowledge Graph Completion (2025.coling-main)

Challenge:	Knowledge Graph Completion (KGC) often requires both KG structural and textual information to be effective.
Approach:	They propose a system which tunes the parameters of Conditional Soft Prompts generated by entities and relations representations to maintain a balance between textual and structural knowledge.
Outcome:	The proposed components outperform baseline models on three static and temporal benchmarks.

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Ben Liu, Jihai Zhang, Fangquan Lin, Cheng Yang, Min Peng

GS-Quant: Granular Semantic and Generative Structural Quantization for Knowledge Graph Completion (2026.acl-long)

Challenge:	Empirical evidence suggests that LLMs perform worse than conventional KGC approaches.
Approach:	They propose a filter-then-generate paradigm and a multiple-choice question format to harness the capability of LLMs while mitigating the issue casused by hallucinations.
Outcome:	The proposed method achieves substantial performance gain compared to existing state-of-the-art methods.

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Qizhuo Xie, Yunhui Liu, Yu Xing, Qianzi Hou, Xudong Jin, Tao Zheng, Tieke He

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

Challenge:	Existing quantization-based approaches to knowledge Graph Completion (KGC) are incomplete.
Approach:	They propose a framework that generates semantically coherent discrete codes for KG entities . they introduce a Granular Semantic Enhancement module that injects hierarchical knowledge into the codebook .
Outcome:	The proposed framework outperforms existing text-based and embedding-based baselines in the KGC domain.

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

A Unified Joint Approach with Topological Context Learning and Rule Augmentation for Knowledge Graph Completion (2024.findings-acl)

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

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Jingtao Guo, Chunxia Zhang, Lingxi Li, Xiaojun Xue, Zhendong Niu

Joint Completion and Alignment of Multilingual Knowledge Graphs (2022.emnlp-main)

Challenge:	Existing knowledge graph completion methods perform simple linear update on relation representation, and only local neighborhood information is aggregated, making it difficult to capture logic semantic between relations and global topological context information.
Approach:	They propose a joint approach with Topological Context learning and Rule Augmentation (TCRA) it uses a topological context learning mechanism and a relation rule context learning system .
Outcome:	The proposed approach performs better on three benchmark datasets and is widely used in knowledgeintensive applications.

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Soumen Chakrabarti, Harkanwar Singh, Shubham Lohiya, Prachi Jain, null Mausam

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

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.

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Changyi Xiao, Yixin Cao

MoCoKGC: Momentum Contrast Entity Encoding for Knowledge Graph Completion (2024.emnlp-main)

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

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Qingyang Li, Yanru Zhong, Yuchu Qin

KC-GenRe: A Knowledge-constrained Generative Re-ranking Method Based on Large Language Models for Knowledge Graph Completion (2024.lrec-main)

Challenge:	Existing approaches to knowledge graph completion have not integrated the structural attributes of knowledge graphs with the textual descriptions of entities to generate robust entity encodings.
Approach:	They propose to integrate structural information from knowledge graphs with textual descriptions of entities to generate robust entity encodings.
Outcome:	The proposed model improves on the standard evaluation metric, Mean Reciprocal Rank (MRR), while surpassing the current best model on the Wikidata5M dataset.

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Yilin Wang, Minghao Hu, Zhen Huang, Dongsheng Li, Dong Yang, Xicheng Lu

CompleQA: Benchmarking the Impacts of Knowledge Graph Completion Methods on Question Answering (2023.findings-emnlp)

Challenge:	Knowledge graph completion (KGC) is a critical task to predict missing facts among entities.
Approach:	They propose a knowledge-constrained generative re-ranking method based on generative large language models for KGC that can predict missing facts among entities.
Outcome:	The proposed method achieves state-of-the-art performance on four datasets and 9.0% and 11.1% compared to the previous methods.

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Donghan Yu, Yu Gu, Chenyan Xiong, Yiming Yang

Double-Branch Multi-Attention based Graph Neural Network for Knowledge Graph Completion (2023.acl-long)

Challenge:	Existing studies have focused on Knowledge Graph Completion as an end in itself, neglecting its potential impact on subsequent applications.
Approach:	They propose a benchmark to assess the impact of representative KGC methods on Knowledge Graph Question Answering (KGQA) they use a knowledge graph with 3 million triplets across 5 distinct domains to evaluate their results.
Outcome:	The proposed benchmark compares four well-known methods with two state-of-the-art systems to assess the impact of incomplete knowledge graphs on KGQA.

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Hongcai Xu, Junpeng Bao, Wenbo Liu

Joint Pre-Encoding Representation and Structure Embedding for Efficient and Low-Resource Knowledge Graph Completion (2024.emnlp-main)

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.

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Chenyu Qiu, Pengjiang Qian, Chuang Wang, Jian Yao, Li Liu, Fang Wei, Eddie Eddie

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

Challenge:	Existing knowledge graph completion models require longer training and inference times as well as increased memory usage.
Approach:	They propose to encode textual descriptions into semantic representations before training and integrate structural embedding with pre-encoded semantic description to improve model's prediction performance on 1-N relations.
Outcome:	The proposed model increases inference speed by 30x and reduces training memory by approximately 60% on the WN18RR and UMLS datasets.

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

Distance-Based Propagation for Efficient Knowledge Graph Reasoning (2023.emnlp-main)

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

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Harry Shomer, Yao Ma, Juanhui Li, Bo Wu, Charu Aggarwal, Jiliang Tang

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

Challenge:	Knowledge graph completion (KGC) aims to predict unseen edges in knowledge graphs (KGs) . a few recent attempts to address this problem sacrifice the performance to gain efficiency.
Approach:	They propose a method that aggregates path information to solve this problem by aggregating paths in a fixed window for each source-target pair.
Outcome:	The proposed method can cut down on the number of propagated messages by 90% while achieving competitive performance on multiple KG datasets.

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

Beyond Completion: A Foundation Model for General Knowledge Graph Reasoning (2025.findings-acl)

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

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Yin Hua, Zhiqiang Liu, Mingyang Chen, Zheng Fang, Chi Man Wong, Lingxiao Li, Chi Man Vong, Huajun Chen, Wen Zhang

Enhancing Large Language Model for Knowledge Graph Completion via Structure-Aware Alignment-Tuning (2025.emnlp-main)

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.

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Yu Liu, Yanan Cao, Xixun Lin, Yanmin Shang, Shi Wang, Shirui Pan

Prior Relational Schema Assists Effective Contrastive Learning for Inductive Knowledge Graph Completion (2024.lrec-main)

Challenge:	Existing knowledge graph completion methods ignore inconsistent representation spaces between natural language and graph structures, leading to duplicate works and time-consuming processes.
Approach:	They propose a framework that enhances LLMs for KGC via structure-aware alignment-tuning to align graph embeddings with the natural language space through multi-task contrastive learning.
Outcome:	The proposed framework outperforms state-of-the-art methods on two KGC tasks across four benchmark datasets.

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Ruilin Luo, Jiayi Li, Jianghangfan Zhang, Jing Xiao, Yujiu Yang

Learning to Think on Hypergraph: HyperCoT for Structure-Guided N-ary Knowledge Graph Completion (2026.acl-long)

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.

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Mengxue Yang, Jinming Li, Chun Yang, Jiaqi Zhu, Jiafan Li, Guanhua Zhang, Ying Li

MAKI: Multi-layer Aligned Knowledge Injection for Structure-aware Knowledge Graph Completion with Large Language Models (2026.findings-acl)

Challenge:	Existing methods to solve knowledge hypergraph link prediction problem are limited by their ability to generate chain-of-thought (CoT) representations.
Approach:	They propose a structure-aware approach that models multi-hop structural reasoning as a depth-sensitive progressive evidence accumulation process.
Outcome:	Experiments on three real-world datasets show that HyperCoT outperforms strong n-ary KGC baselines while yielding interpretable multi-hop reasoning traces.

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Zhiwen Xie, Xin Wang, Guangyou Zhou, Derek F. Wong