论文标题
知识图中的实体分析
Entity Profiling in Knowledge Graphs
论文作者
论文摘要
知识图（kgs）是图形结构的知识库，可存储有关现实世界实体的事实信息。了解每个实体的独特性对于KGS的分析，共享和重复使用至关重要。传统的分析技术包括各种各样的方法，可以在各种应用中找到独特的特征，这可以帮助在人类对KGS的理解过程中区分实体。在这项工作中，我们提出了一种新颖的分析方法，以识别独特的实体特征。特征的独特性是通过一个模型仔细测量的，这是一种可扩展的表示模型，以产生多模式实体嵌入。我们全面评估了由真正的KGS产生的实体概况的质量。结果表明，我们的方法促进了人类对公斤实体的理解。
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.
