Entity Set Expansion with Meta Path in Knowledge Graph

Abstract

Entity set expansion (ESE) is the problem that expands a small set of seed entities into a more complete set, entities of which have common traits. As a popular data mining task, ESE has been widely used in many applications, such as dictionary construction and query suggestion. Contemporary ESE mainly utilizes text and Web information. That is, the intrinsic relation among entities is inferred from their occurrences in text or Web. With the surge of knowledge graph in recent years, it is possible to extend entities according to their occurrences in knowledge graph. In this paper, we consider the knowledge graph as a heterogeneous information network (HIN) that contains different types of objects and links, and propose a novel method, called MP_ESE, to extend entities in the HIN. The MP_ESE employs meta paths, a relation sequence connecting entities, in HIN to capture the implicit common traits of seed entities, and an automatic meta path generation method, called SMPG, is provided to exploit the potential relations among entities. With these generated and weighted meta paths, the MP_ESE can effectively extend entities. Experiments on real datasets validate the effectiveness of MP_ESE.