Topic Maps - An Enabling Technology for Knowledge Management

Wolfgang Essmayr, Knud Steiner, Roland Wagner

Research output: Chapter in Book/Report/Conference proceedingConference proceedingspeer-review

Abstract

In this paper we show how topic maps (semantically structured, self-describing link networks standardized by ISO/IEC 13250) can be used to represent superimposed information as well as domain ontologies ? two established approaches to knowledge management systems. Superimposed information enriches explicit knowledge resources for purposes like retrieval or connection without modifying this base information. Domain ontologies are frequently used to capture and formalize implicit domain-specific knowledge. Since the underlying abstract model of topic maps provides a high degree of power and flexibility, topic maps can be also used to combine both approaches and provide a framework that supports the evolutionary construction of organizational memories able to grow both in structure and extent.
Original languageEnglish
Title of host publicationProceedings of the 12th International Workshop on Database and Expert Systems Applications (DEXA 2001)
EditorsA. M. Tjoa, R.R. Wagner
Place of PublicationMunich, Germany
PublisherIEEE Computer Society
Pages472-476
Number of pages5
ISBN (Electronic)0769512305
ISBN (Print)0-7695-1230-5
DOIs
Publication statusPublished - Sept 2001

Publication series

NameProceedings - International Workshop on Database and Expert Systems Applications, DEXA
Volume2001-January
ISSN (Print)1529-4188

Fields of science

  • 102001 Artificial intelligence
  • 102006 Computer supported cooperative work (CSCW)
  • 102010 Database systems
  • 102014 Information design
  • 102015 Information systems
  • 102016 IT security
  • 102028 Knowledge engineering
  • 102019 Machine learning
  • 102022 Software development
  • 102025 Distributed systems
  • 502007 E-commerce
  • 505002 Data protection
  • 506002 E-government
  • 509018 Knowledge management
  • 202007 Computer integrated manufacturing (CIM)
  • 102033 Data mining
  • 102035 Data science

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