Discovering subsumption relationships for web-based ontologies

Abstract

As search engines are becoming smarter at interpreting user queries and providing meaningful responses, they rely on ontologies to understand the meaning of entities. Creating ontologies manually is a laborious process, and resulting ontologies may not reflect the way users think about the world, as many concepts used in queries are noisy, and not easily amenable to formal modeling. There has been considerable effort in generating ontologies from Web text and query streams, which may be more reective of how users query and write content. In this paper, we describe the Latte system that automatically generates a subconcept- superconcept hierarchy, which is critical for using ontologies to answer queries. Latte combines signals based on word-vector representations of concepts and dependency parse trees; however, Latte derives most of its power from an ontology of attributes extracted from the Web that indicates the aspects of concepts that users find important. Latte achieves an F1 score of 74%, which is comparable to expert agreement on a similar task. We additionally demonstrate the usefulness of Latte in detecting high quality concepts from an existing resource of IsA links.