EgoSet: Exploiting Word Ego-networks and User-generated Ontology for Multifaceted Set Expansion

A key challenge of entity set expansion is that multifaceted input seeds can lead to significant incoherence in the result set. In this paper, we present a novel solution to handling multifaceted seeds by combining existing user-generated ontologies with a novel wordsimilarity metric based on skip-grams. By blending the two resources we are able to produce sparse word ego-networks that are centered on the seed terms and are able to capture semantic equivalence among words. We demonstrate that the resulting networks possess internally-coherent clusters, which can be exploited to provide non-overlapping expansions, in order to reflect different semantic classes of the seeds. Empirical evaluation against state-ofthe-art baselines shows that our solution, EgoSet, is able to not only capture multiple facets in the input query, but also generate expansions for each facet with higher precision. CCS Concepts •Computing methodologies → Information extraction; Semantic networks; •Inform...

• Type: Journal
• Year: 2016
• Where: WSDM
• Authors: Xin Rong, Zhe Chen, Qiaozhu Mei, Eytan Adar
• Keywords: Data Mining, WSDM 2016