OMCSNet: A Commonsense Inference Toolkit
Hugo Liu · 2003
Large, easy-to-use semantic networks of symbolic linguistic knowledge such as WordNet and MindNet have become staple resources for semantic analysis tasks from query expansion to word-sense disambiguation. However, the knowledge captured by these resources is limited to formal taxonomic relations between or dictionary definitions of lexical items. While such knowledge is sufficient for some NLP tasks, we believe that broader opportunities are afforded by databases containing more diverse kinds of world knowledge, including substantial knowledge about compound concepts like activities (e.g. “washing hair”), accompanied by a richer set of temporal, spatial, functional, and social relations between concepts. Based on this premise, we introduce OMCSNet, a freely available, large semantic network of commonsense knowledge. Built from the Open Mind Common Sense corpus, which acquires world knowledge from a webbased community of instructors, OMCSNet is presently a semantic network of 280,000 items of common-sense knowledge, and a set of tools for making inferences using this knowledge. In this paper, we describe OMCSNet, evaluate it in the context of other semantic knowledge bases, and review how OMCSNet has been used to enable and improve various NLP tasks. 1