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Finding Associative Entities in Knowledge Graph by Incorporating User Behaviors

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Udgivet i:Journal of Database Management vol. 36, no. 1 (2025), p. 1-25
Hovedforfatter: Li, Jianyu
Andre forfattere: Yang, Peizhong, Yue, Kun, Duan, Liang, Huang, Zehao
Udgivet:
IGI Global
Fag:
Algorithms
Weighting
User behavior
Experiments
Graphs
Associations
Candidates
Greedy algorithms
Climbing
Weighting functions
Knowledge representation
Manuscripts
Competitors
Online adgang:Citation/Abstract
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Resumen: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.
ISSN:1063-8016
1533-8010
1047-9430
DOI:10.4018/JDM.371751
Fuente:ABI/INFORM Global