A Semantically Enhanced Knowledge Discovery Method for Knowledge Graph Based on Adjacency Fuzzy Predicates Reasoning

Pu Li, Guohao Zhou, Zhilei Yin, Rui Chen, Suzhi Zhang

Source Title: International Journal on Semantic Web and Information Systems (IJSWIS)19(1)

ISSN: 1552-6283|EISSN: 1552-6291|EISBN13: 9781668479094|DOI: 10.4018/IJSWIS.323921

MLA

Li, Pu, et al. "A Semantically Enhanced Knowledge Discovery Method for Knowledge Graph Based on Adjacency Fuzzy Predicates Reasoning." IJSWIS vol.19, no.1 2023: pp.1-24. http://doi.org/10.4018/IJSWIS.323921

APA

Li, P., Zhou, G., Yin, Z., Chen, R., & Zhang, S. (2023). A Semantically Enhanced Knowledge Discovery Method for Knowledge Graph Based on Adjacency Fuzzy Predicates Reasoning. International Journal on Semantic Web and Information Systems (IJSWIS), 19(1), 1-24. http://doi.org/10.4018/IJSWIS.323921

Chicago

Li, Pu, et al. "A Semantically Enhanced Knowledge Discovery Method for Knowledge Graph Based on Adjacency Fuzzy Predicates Reasoning," International Journal on Semantic Web and Information Systems (IJSWIS) 19, no.1: 1-24. http://doi.org/10.4018/IJSWIS.323921

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Abstract

Discover the deep semantics from the massively structured data in knowledge graph and provide reasonable explanations are a series of important foundational research issues of artificial intelligence. However, the deep semantics hidden between entities in knowledge graph cannot be well expressed. Moreover, considering many predicates express fuzzy relationships, the existing reasoning methods cannot effectively deal with these fuzzy semantics and interpret the corresponding reasoning process. To counter the above problems, in this article, a new interpretable reasoning schema is proposed by introducing fuzzy theory. The presented method focuses on analyzing the fuzzy semantic between related entities in a knowledge graph. By annotating the fuzzy semantic features of adjacency predicates, a novel semantic reasoning model is designed to realize the fuzzy semantic extension over knowledge graph. The evaluation, based on both visualization and query experiments, shows that this proposal has advantages over the initial knowledge graph and can discover more valid semantic information.