Entity Profiling in Knowledge Graphs

• URL: http://arxiv.org/abs/2003.00172v1
• Date: Sat, 29 Feb 2020 03:44:24 GMT
• Title: Entity Profiling in Knowledge Graphs
• Authors: Xiang Zhang, Qingqing Yang, Jinru Ding and Ziyue Wang
• Abstract summary: We present a novel profiling approach to identify distinctive entity features. The distinctiveness of features is carefully measured by a HAS model. We fully evaluate the quality of entity profiles generated from real Knowledge Graphs.
• Score: 5.582713124168685
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge Graphs (KGs) are graph-structured knowledge bases storing factual information about real-world entities. Understanding the uniqueness of each entity is crucial to the analyzing, sharing, and reusing of KGs. Traditional profiling technologies encompass a vast array of methods to find distinctive features in various applications, which can help to differentiate entities in the process of human understanding of KGs. In this work, we present a novel profiling approach to identify distinctive entity features. The distinctiveness of features is carefully measured by a HAS model, which is a scalable representation learning model to produce a multi-pattern entity embedding. We fully evaluate the quality of entity profiles generated from real KGs. The results show that our approach facilitates human understanding of entities in KGs.

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