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Expansion of Tail Concept Using Web Tables

Human-curated knowledgebases like Freebase and DBPedia cover popular concepts such as persons, organizations and locations, but many more specific concepts fall into the long tail outside current knowledgebases, such as acidic fruits, HD video formats and renewable resources. These concepts are found in conceptentity pairs automatically extracted from text documents, but they cover a limited number of entities per concept due to insufficient mentions in text. In this paper, we propose to expand the coverage of entities for tail concepts using web tables. It is challenging to apply existing techniques because a tail concept often overlaps many other concepts. Our solution is based on the fact that many web tables contain a set of entities all belonging to a fine grained concept. We leverage the rich content signals and the structured entities in web tables to infer such exclusive tables. Our inference differs from previous label propagation methods via modeling a special table-entity relationship. It reduces semantic drift without using a reference ontology. Taking concept name and two seed entities as only input, it can augment thousands of popular tail concepts with 85 times recall boost on average, while maintaining over 90% precision.