Automatically Learning Robot Domain Ontology from Collective Knowledge for Home Service Robots

Dongyeop Kang, Eugene Seo, Sookyung Kim, Ho Jin Choi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Today, for enabling intelligent decision and high accuracy of recognition in service robots, many researchers supplement robot's knowledge model using the additional knowledge. However, the construction of them knowledge requiring much effort and domain experts fully depends on man power by few people. Thus, this paper proposes a fully automated process of acquiring domain knowledge and representing them to efficient and semantically abundant structure. Thus, we investigate the characteristics of OMICS as preceding case study for collective knowledge in robot domain, and describe the automated process of conversion of such collective knowledge to robot domain ontology. Also, we suggest dynamic semantic distribution method to solve appropriate generalization of relation problem. Finally, we evaluate the efficiency and semantic of our structure for the ontology compared to other knowledge bases for robots.

Original languageEnglish (US)
Title of host publication11th International Conference on Advanced Communication Technology, ICACT 2009 - Proceedings
Pages1766-1771
Number of pages6
StatePublished - 2009
Externally publishedYes
Event11th International Conference on Advanced Communication Technology, ICACT 2009 - Phoenix Park, Korea, Republic of
Duration: Feb 15 2009Feb 18 2009

Publication series

NameInternational Conference on Advanced Communication Technology, ICACT
Volume3
ISSN (Print)1738-9445

Conference

Conference11th International Conference on Advanced Communication Technology, ICACT 2009
Country/TerritoryKorea, Republic of
CityPhoenix Park
Period2/15/092/18/09

Keywords

  • Collective knowledge
  • OMICS
  • Ontology
  • Ontology learning

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