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Entity Set Expansion with Semantic Features of Knowledge Graphs

14 Pages Posted: 25 Sep 2018 First Look: Accepted

See all articles by Jun Chen

Jun Chen

Renmin University of China - School of Information

Yueguo Chen

Renmin University of China - School of Information

Xiangling Zhang

Renmin University of China - School of Information

Xiaoyong Du

Renmin University of China - School of Information

Ke Wang

Simon Fraser University (SFU) - School of Computing Science

Ji-Rong Wen

Renmin University of China - School of Information

Abstract

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.

Keywords: Knowledge Graph, Semantic Feature, Entity Set Expansion, Semantic Search, Ranking Model

Suggested Citation

Chen, Jun and Chen, Yueguo and Zhang, Xiangling and Du, Xiaoyong and Wang, Ke and Wen, Ji-Rong, Entity Set Expansion with Semantic Features of Knowledge Graphs (September 24, 2018). Journal of Web Semantics First Look . Available at SSRN: https://ssrn.com/abstract=3254297 or http://dx.doi.org/10.2139/ssrn.3254297

Jun Chen (Contact Author)

Renmin University of China - School of Information

Room B906
Xianjin Building
Beijing, Beijing 100872
China

Yueguo Chen

Renmin University of China - School of Information ( email )

Room B906
Xianjin Building
Beijing, Beijing 100872
China

Xiangling Zhang

Renmin University of China - School of Information

Room B906
Xianjin Building
Beijing, Beijing 100872
China

Xiaoyong Du

Renmin University of China - School of Information ( email )

Room B906
Xianjin Building
Beijing, Beijing 100872
China

Ke Wang

Simon Fraser University (SFU) - School of Computing Science

Burnaby, V5A 1S6
United States

Ji-Rong Wen

Renmin University of China - School of Information

Room B906
Xianjin Building
Beijing, Beijing 100872
China

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