Multi-Task Learning for Knowledge Graph Completion with Pre-trained Language Models

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

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Challenge

Approach

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

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

Semantic Text Processing

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.

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

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Rongchuan Tang, Yang Zhao, Chengqing Zong, Yu Zhou

Multimodality

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.

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

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

Semantic Text Processing

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.

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

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

Semantic Text Processing

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

A Framework for Adapting Pre-Trained Language Models to Knowledge Graph Completion (2022.emnlp-main)

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Justin Lovelace, Carolyn Rosé

Semantic Text Processing

Challenge:	Recent work has demonstrated that entity representations can be extracted from pre-trained language models to develop knowledge graph completion models that are more robust to the naturally occurring sparsity found in knowledge graphs.
Approach:	They propose unsupervised and supervised methods to extract more informative representations from pre-trained language models to develop knowledge graph completion models.
Outcome:	The proposed model outperforms recent neural models in terms of performance and unsupervised processing methods.

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

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

Semantic Text Processing

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.

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

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Yusuke Sakai, Hidetaka Kamigaito, Katsuhiko Hayashi, Taro Watanabe

Semantic Text Processing

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.

Text Augmented Open Knowledge Graph Completion via Pre-Trained Language Models (2023.findings-acl)

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Pengcheng Jiang, Shivam Agarwal, Bowen Jin, Xuan Wang, Jimeng Sun, Jiawei Han

Semantic Text Processing

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.

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

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

Semantic Text Processing

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.

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

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

Semantic Text Processing

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.