Linking Entities in Short Texts Based on a Chinese Semantic Knowledge Base

Abstract

Populating existing knowledge base with new facts is important to keep the knowledge base fresh and most updated. Before importing new knowledge into the knowledge base, entity linking is required so that the entities in the new knowledge can be linked to the entities in the knowledge base. During this process, entity disambiguation is the most challenging task. There have been many studies on leveraging name ambiguity problem via a variety of algorithms. In this paper, we propose an entity linking method based on Chinese Semantic Knowledge where entity disambiguation can be addressed by retrieving a variety of semantic relations and analyzing the corresponding documents with similarity measurement. Based on the proposed method, we developed CASIA_EL, a system for linking entities with knowledge bases. We validate the proposed method by linking 1232 entities mined from Sina Weibo to a Chinese Semantic knowledge base, resulting in an accuracy of 88.5%. The results show that the CASIA_EL system and the proposed algorithm are potentially effective.