@inproceedings{li-etal-2025-mutual,
title = "A Mutual Information Perspective on Knowledge Graph Embedding",
author = "Li, Jiang and
Su, Xiangdong and
Duo, Zehua and
Lan, Tian and
Guo, Xiaotao and
Gao, Guanglai",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.1077/",
doi = "10.18653/v1/2025.acl-long.1077",
pages = "22152--22166",
ISBN = "979-8-89176-251-0",
abstract = "Knowledge graph embedding techniques have emerged as a critical approach for addressing the issue of missing relations in knowledge graphs. However, existing methods often suffer from limitations, including high intra-group similarity, loss of semantic information, and insufficient inference capability, particularly in complex relation patterns such as 1-N and N-1 relations. To address these challenges, we introduce a novel KGE framework that leverages mutual information maximization to improve the semantic representation of entities and relations. By maximizing the mutual information between different components of triples, such as $(h, r)$ and $t$, or $(r, t)$ and $h$, the proposed method improves the model{'}s ability to preserve semantic dependencies while maintaining the relational structure of the knowledge graph. Extensive experiments on benchmark datasets demonstrate the effectiveness of our approach, with consistent performance improvements across various baseline models. Additionally, visualization analyses and case studies demonstrate the improved ability of the MI framework to capture complex relation patterns."
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<abstract>Knowledge graph embedding techniques have emerged as a critical approach for addressing the issue of missing relations in knowledge graphs. However, existing methods often suffer from limitations, including high intra-group similarity, loss of semantic information, and insufficient inference capability, particularly in complex relation patterns such as 1-N and N-1 relations. To address these challenges, we introduce a novel KGE framework that leverages mutual information maximization to improve the semantic representation of entities and relations. By maximizing the mutual information between different components of triples, such as (h, r) and t, or (r, t) and h, the proposed method improves the model’s ability to preserve semantic dependencies while maintaining the relational structure of the knowledge graph. Extensive experiments on benchmark datasets demonstrate the effectiveness of our approach, with consistent performance improvements across various baseline models. Additionally, visualization analyses and case studies demonstrate the improved ability of the MI framework to capture complex relation patterns.</abstract>
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%0 Conference Proceedings
%T A Mutual Information Perspective on Knowledge Graph Embedding
%A Li, Jiang
%A Su, Xiangdong
%A Duo, Zehua
%A Lan, Tian
%A Guo, Xiaotao
%A Gao, Guanglai
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F li-etal-2025-mutual
%X Knowledge graph embedding techniques have emerged as a critical approach for addressing the issue of missing relations in knowledge graphs. However, existing methods often suffer from limitations, including high intra-group similarity, loss of semantic information, and insufficient inference capability, particularly in complex relation patterns such as 1-N and N-1 relations. To address these challenges, we introduce a novel KGE framework that leverages mutual information maximization to improve the semantic representation of entities and relations. By maximizing the mutual information between different components of triples, such as (h, r) and t, or (r, t) and h, the proposed method improves the model’s ability to preserve semantic dependencies while maintaining the relational structure of the knowledge graph. Extensive experiments on benchmark datasets demonstrate the effectiveness of our approach, with consistent performance improvements across various baseline models. Additionally, visualization analyses and case studies demonstrate the improved ability of the MI framework to capture complex relation patterns.
%R 10.18653/v1/2025.acl-long.1077
%U https://aclanthology.org/2025.acl-long.1077/
%U https://doi.org/10.18653/v1/2025.acl-long.1077
%P 22152-22166
Markdown (Informal)
[A Mutual Information Perspective on Knowledge Graph Embedding](https://aclanthology.org/2025.acl-long.1077/) (Li et al., ACL 2025)
ACL
- Jiang Li, Xiangdong Su, Zehua Duo, Tian Lan, Xiaotao Guo, and Guanglai Gao. 2025. A Mutual Information Perspective on Knowledge Graph Embedding. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 22152–22166, Vienna, Austria. Association for Computational Linguistics.