How much well does organizational knowledge transfer work with domain and rule ontologies?

Keido Kobayashi, Akiko Yoshioka, Masao Okabe, Masahiko Yanagisawa, Hiroshi Yamazaki, Takahira Yamaguchi

研究成果: Conference contribution

1 引用 (Scopus)

抜粋

Knowledge transfer to next-generation engineers is an urgent issue in Japan. In this paper, we propose a new approach without costly OJT (on-the-job training), that is, combinational usage of domain and rule ontologies and a rule-based system. A domain ontology helps novices understand the exact meaning of the engineering rules and a rule ontology helps them get the total picture of the knowledge. A rule-based system helps domain experts externalize their tacit knowledge to ontologies and also helps novices internalize them. As a case study, we applied our proposal to some actual job. We also did an evaluation experiment for this case study and have confirmed that our proposal is effective.

元の言語English
ホスト出版物のタイトルKnowledge Science, Engineering and Management - Third International Conference, KSEM 2009, Proceedings
ページ382-393
ページ数12
DOI
出版物ステータスPublished - 2009 12 1
イベント3rd International Conference on Knowledge Science, Engineering and Management, KSEM 2009 - Vienna, Austria
継続期間: 2009 11 252009 11 27

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
5914 LNAI
ISSN(印刷物)0302-9743
ISSN(電子版)1611-3349

Other

Other3rd International Conference on Knowledge Science, Engineering and Management, KSEM 2009
Austria
Vienna
期間09/11/2509/11/27

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • これを引用

    Kobayashi, K., Yoshioka, A., Okabe, M., Yanagisawa, M., Yamazaki, H., & Yamaguchi, T. (2009). How much well does organizational knowledge transfer work with domain and rule ontologies?Knowledge Science, Engineering and Management - Third International Conference, KSEM 2009, Proceedings (pp. 382-393). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 巻数 5914 LNAI). https://doi.org/10.1007/978-3-642-10488-6_37