Web mining for event-based commonsense knowledge using lexico-syntactic pattern matching and semantic role labeling

Authors:
Sheng-Hao Hung;Chia-Hung Lin;Jen-Shin Hong
Affiliations:
Department of Computer Science and Information Engineering, National ChiNan University, Taiwan;Department of Management Sciences, R.O.C. Military Academy, Taiwan;Department of Computer Science and Information Engineering, National ChiNan University, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

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Abstract

A sophisticated commonsense knowledgebase is essential for many intelligent system applications. This paper presents a methodology for automatically retrieving event-based commonsense knowledge from the web. The approach is based on matching the text in web search results to designed lexico-syntactic patterns. We apply a semantic role labeling technique to parse the extracted sentences so as to identify the essential knowledge associated with the event(s) described in each sentence. Particularly, we propose a semantic role substitution strategy to prune knowledge items that have a high probability of erroneously parsed semantic roles. The experimental results in a case study for retrieving the knowledge is ''capable of'' shows that the accuracy of the retrieved commonsense knowledge is around 98%.