Knowledge graph completion (KGC) aims to reason over known facts and infer the missing links. Text-based methods such as KGBERT (Yao et al., 2019) learn entity representations from natural language descriptions, and have the potential for inductive KGC. However, the performance of text-based methods still largely lag behind graph embedding-based methods like TransE (Bordes et al., 2013) and RotatE (Sun et al., 2019b). In this paper, we identify that the key issue is efficient contrastive learning. To improve the learning efficiency, we introduce three types of negatives: in-batch negatives, pre-batch negatives, and self-negatives which act as a simple form of hard negatives. Combined with InfoNCE loss, our proposed model SimKGC can substantially outperform embedding-based methods on several benchmark datasets. In terms of mean reciprocal rank (MRR), we advance the state-of-the-art by +19% on WN18RR, +6.8% on the Wikidata5M transductive setting, and +22% on the Wikidata5M inductive setting. Thorough analyses are conducted to gain insights into each component. Our code is available at https://github.com/intfloat/SimKGC .
SLiNT: Structure-aware Language Model with Injection and Contrastive Training for Knowledge Graph Completion
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；National Astronomical Observatories, Chinese Academy of Sciences（中国科学院国家天文台）
KERMIT: Knowledge Graph Completion of Enhanced Relation Modeling with Inverse Transformation
Research Institute of Cyberspace Security, Harbin Institute of Technology（哈尔滨工业大学网络空间安全研究院）；School of Computer Science and Technology, Harbin Institute of Technology at Weihai（哈尔滨工业大学（威海）计算机科学与技术学院）；Shandong Key Laboratory of Industrial Network Security（山东省工业网络安全重点实验室）；Department of Mathematics, Hong Kong Baptist University（香港浸会大学数学系）
Enhancing Large Language Model for Knowledge Graph Completion via Structure-Aware Alignment-Tuning
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Griffith University（格里菲斯大学）
College of Computer Science\ , TMCC, TBI Center\ University Tianjin China；School of Software\ University Tianjin China；College of Computer Science\ , TMCC, TBI Center\ University；School of Software\ University
Multi-task Pre-training Language Model for Semantic Network Completion
Tencent（腾讯）；Beijing Institute of Microbiology and Epidemiology（北京微生物与流行病学研究所）；State Key Laboratory of Pathogen and Biosecurity（病原与生物安全国家重点实验室）；National University of Defense Technology（国防科技大学）；National Key Lab of Parallel and Distributed Processing（并行与分布式处理国家重点实验室）
