Commonsense knowledge extraction for persian language: a combinatory approach

Moradi, M ; Sharif University of Technology | 2015

246 Viewed
  1. Type of Document: Article
  2. Publisher: Iranian Research Institute for Scientific Information and Documentation , 2015
  3. Abstract:
  4. The Putting human commonsense knowledge into computers has always been a long standing dream of artificial intelligence (AI). The cost of several tens of millions of dollars and time have been covered so that the computers could know about "objects falling, not rising.", "running is faster than walking". The large database was built, automated and semi-automated methods were introduced and volunteers' efforts were utilized to achieve this, but an automated, high-throughput and low-noise method for commonsense collection still remains as the holy grail of AI. The aim of this study was to build commonsense knowledge ontology using three approaches namely Hearst method, machine translation and using structured resources. Using three Persian corpuses and applying aforementioned methods, we could extract 7 different relations. Seventy thousand assertions have been extracted. Finally, average accuracy of Hearst, MT and structured resource were 75 per cent, 75 per cent and 100 per cent respectively
  5. Keywords:
  6. Commonsense Knowledge ; Ontology ; Relation Extraction
  7. Source: Iranian Journal of Information Processing Management ; Volume 31, Issue 1 , 2015 , Pages 109-124 ; 22518223 (ISSN)
  8. URL: http://en.journals.sid.ir/ViewPaper.aspx?ID=471551