Organizational knowledge transfer using ontologies and a rule-based system

Masao Okabe, Akiko Yoshioka, Keido Kobayashi, Takahira Yamaguchi

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

In recent automated and integrated manufacturing, socalled intelligence skill is becoming more and more important and its efficient transfer to next-generation engineers is one of the urgent issues. In this paper, we propose a new approach without costly OJT (on-the-job training), that is, combinational usage of a domain ontology, a rule ontology and a rule-based system. Intelligence skill can be decomposed into pieces of simple engineering rules. A rule ontology consists of these engineering rules as primitives and the semantic relations among them. A domain ontology consists of technical terms in the engineering rules and the semantic relations among them. A rule ontology helps novices get the total picture of the intelligence skill and a domain ontology helps them understand the exact meanings of the engineering rules. A rule-based system helps domain experts externalize their tacit intelligence skill to ontologies and also helps novices internalize them. As a case study, we applied our proposal to some actual job at a remote control and maintenance office of hydroelectric power stations in Tokyo Electric Power Co., Inc. We also did an evaluation experiment for this case study and the result supports our proposal.

Original languageEnglish
Pages (from-to)763-773
Number of pages11
JournalIEICE Transactions on Information and Systems
VolumeE93-D
Issue number4
DOIs
Publication statusPublished - 2010

Keywords

  • Domain ontology
  • Intelligence skill
  • Knowledge management
  • Knowledge transfer
  • Rule ontology
  • Rule-based system
  • Scheduling

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence

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