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Article type: Research Article
Authors: Xu, You-Wei | Zhang, Hong-Jun* | Cheng, Kai | Liao, Xiang-Lin | Zhang, Zi-Xuan* | Li, Yun-Bo
Affiliations: Army Engineering University of PLA, Nanjing, Jiangsu, China
Correspondence: [*] Corresponding authors: Hong-Jun Zhang and Zi-Xuan Zhang, Army Engineering University of PLA, Nanjing, Jiangsu, China. E-mail: [email protected] and E-mail: [email protected].
Abstract: Knowledge graph embedding is aimed at capturing the semantic information of entities by modeling the structural information between entities. For long-tail entities which lack sufficient structural information, general knowledge graph embedding models often show relatively low performance in link prediction. In order to solve such problems, this paper proposes a general knowledge graph embedding framework to learn the structural information as well as the attribute information of the entities simultaneously. Under this framework, a H-AKRL (Hypergraph Neural Networks based Attribute-embodied Knowledge Representation Learning) model is put forward, where the hypergraph neural network is used to model the correlation between entities and attributes at a higher level. The complementary relationship between attribute information and structural information is taken full advantage of, enabling H-AKRL to finally achieve the goal of improving link prediction performance. Experiments on multiple real-world data sets show that the H-AKRL model has significantly improved the link prediction performance, especially in the embeddings of long tail entities.
Keywords: Attribute network, hypergraph neural network, knowledge graph embedding, long-tail entity
DOI: 10.3233/IDA-216007
Journal: Intelligent Data Analysis, vol. 26, no. 4, pp. 959-975, 2022
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