Entity Profiling in Knowledge Graphs
This work addresses the challenge of differentiating entities in knowledge graphs for improved analysis and reuse, though it appears incremental as it builds on traditional profiling methods.
The paper tackles the problem of understanding entity uniqueness in knowledge graphs by introducing a novel profiling approach that identifies distinctive entity features using a scalable HAS model, and demonstrates its effectiveness in facilitating human understanding of entities in real KGs.
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.