Entity Set Expansion with Semantic Features of Knowledge Graphs

Jun Chen, Yueguo Chen, Xiangling Zhang, Xiaoyong Du, Ke Wang, Ji-Rong Wen


A large-scale knowledge graph contains a huge number of path-based semantic features, which provides a flexible mechanism to assign and expand semantics/attributes to entities. A particular set of these semantic features can be exploited on the fly, to support particular entity-oriented semantic search tasks. In this paper, we use entity set expansion as an example to show how these path-based semantic features can be effectively utilized in a semantic search application. The entity set expansion problem is to expand a small set of seed entities to a more complete set of similar entities. Traditionally, people solve this problem by exploiting the statistical co-occurrence of entities in the web pages, where semantic correlation among the seed entities is not well exploited. We propose to address the entity set expansion problem using the path-based semantic features of knowledge graphs. Our method first discovers relevant semantic features of the seed entities, which can be treated as the common aspects of these seed entities, and then retrieves relevant entities based on the discovered semantic features. Probabilistic models are proposed to rank entities, as well as semantic features, by handling the incompleteness of knowledge graphs. Extensive experiments on a public knowledge graph (i.e., DBpedia V3.9) and three public test collections (i.e., CLEF-QALD 2-4, SemSearch-LS 2011, and INEX-XER 2009) show that our method significantly outperforms the state-of-the-art techniques.

Full Text: Untitled
Type of Paper: Research Paper
Keywords: Knowledge Graph, Semantic Feature, Entity Set Expansion, Semantic Search, Ranking Model
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