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.
@article{arxiv.2003.00172,
title = {Entity Profiling in Knowledge Graphs},
author = {Xiang Zhang and Qingqing Yang and Jinru Ding and Ziyue Wang},
journal= {arXiv preprint arXiv:2003.00172},
year = {2020}
}