A Semi-automatic Approach to Extracting Common Sense Knowledge from Knowledge Sources

Abstract

AbstractCommon sense knowledge based systems are developed by researchers to enable machines to understand ordinary knowledge and reason intelligently as a human would. The knowledge repositories of such systems are usually developed manually by a knowledge engineer or by users. Building a knowledge base of common sense knowledge such as that possessed by an average human being would be a very time-consuming, if not impossible, task. Some aspects of real world knowledge have already been captured and organized into various repositories such as the World Wide Web, WordNet, and the DAML ontology library. However, the extraction and integration of common sense knowledge from those sources remains a challenge. To address this challenge, an architecture for a Common Sense Knowledge Extractor is proposed that serves as an intermediary tool to extract common sense knowledge from several knowledge sources in order to develop a common sense repository. The design of the system as an extension of prior research on intelligent query processing is presented.KeywordsCommon SenseQuery ProcessingKnowledge SourceKnowledge ElementCommon Sense KnowledgeThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.