Finding Associative Entities in Knowledge Graph by Incorporating User Behaviors

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Author(s): Jianyu Li (School of Information Science and Engineering, Yunnan University, Kunming, China & Yunnan Key Laboratory of Intelligent Systems and Computing, Yunnan University, Kunming, China), Peizhong Yang (School of Information Science and Engineering, Yunnan University, Kunming, China & Yunnan Key Laboratory of Intelligent Systems and Computing, Yunnan University, Kunming, China), Kun Yue (School of Information Science and Engineering, Yunnan University, Kunming, China & Yunnan Key Laboratory of Intelligent Systems and Computing, Yunnan University, Kunming, China), Liang Duan (School of Information Science and Engineering, Yunnan University, Kunming, China & Yunnan Key Laboratory of Intelligent Systems and Computing, Yunnan University, Kunming, China)and Zehao Huang (School of Information Science and Engineering, Yunnan University, Kunming, China & Yunnan Key Laboratory of Intelligent Systems and Computing, Yunnan University, Kunming, China)
Copyright: 2025
Volume: 36
Issue: 1
Pages: 24
Source title: Journal of Database Management (JDM)
Editor(s)-in-Chief: Keng Siau (Singapore Management University, Singapore)
DOI: 10.4018/JDM.371751

Keywords: Data Mining and Databases / Databases / Engineering Science Reference / Library & Information Science

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Abstract

The task of finding associative entities in knowledge graph (KG) is to provide a ranking list of entities according to their association degrees. However, many entities are not only linked in KG but also associated in terms of user behaviors, which facilitates finding associative entities accurately. This manuscript incorporates KG with user-generated data to propose the Association Entity Graph Model (AEGM) to evaluate the association degrees. They first propose the joint weighting function to evaluate the entity associations and prove its submodularity theoretically as well as the greedy algorithm to select the candidates efficiently. They define the entity association information to score the entity association and give the hill climbing search based algorithm for AEGM construction. Following, they embed AEGM to calculate the association degrees and obtain the associative entities efficiently. Extensive experiments on three datasets show that the proposed method can achieve a better performance than some state-of-the-art competitors in accurately finding associative entities.

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Finding Associative Entities in Knowledge Graph by Incorporating User Behaviors

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