Knowledge Organization System

 
 Editor
Sonia Sánchez-Cuadrado sonia.sanchez.cuadrado@uc3m.es
 Incorporated contributions
Sonia Sanchez-Cuadrado; Jorge Morato
 Usage domain
Information Science
 Type
Concept
 French
Systèmes de organisation de la connaissance
 German WissensOrganisation Systeme
 

The Knowledge Organization System (KOS) provides a framework for the different classification schemes used to organize knowledge. Some KOSs are library classifications, taxonomies, subject headings, thesauri, ontologies, etc. KOS is a corner stone of Knowledge Organization tools.


Knowledge Organization techniques are used to build KOSs. These techniques outline principles to build, manage, and visualize KOS. Knowledge Organization Systems show a simplified view of the concepts of a domain. The goal is provide a way to improve the understanding and the management of a field of knowledge. 


Taking account of the variety of disciplines needs to facilitate their understanding, KO Systems are present in a wide range of fields of knowledge. There are examples of KOS in e-learning, Artificial Intelligence, Software Engineering, and Information Science. Each of these fields gives to KO Systems one or more different names; each KOS has its own design characteristics, according its specific goals. In this manner, e-learning talks about mind maps and concept maps; Artificial Intelligence addresses ontologies and semantic networks; Software Engineering talks about UML diagrams; Information Science uses thesauri, subject headings, library classifications, etc. Although, each approach has a different semantic structure, depending on its goals, all of them develop and maintain a domain vocabulary to represent concepts, and semantic relationships between these concepts.


The construction of a KOS requires a high level of intellectual effort to reach an agreement about the representation. This involves analyzing the domains to extract the main concepts and relationships and to agree these analyses, in order to develop a shared representation. This is laborious and exhausting work with frequent delays. The process can be simplified by using a suitable methodology and software applications that have been developed to facilitate this work. Examples can be found in Software Engineering and Ontology Engineering.   


One of the main bottlenecks is knowledge acquisition. This phase tries to identify the main concepts, by looking at different information sources and seeking the advice of domain experts. The next step is conceptualization, by structuring the domain. This means analyzing terminology, synonyms and hierarchical and associative structures. Also, it is important to identify the constraints of each relation or attribute.


Some approaches have been made to group different KOSs. In this respect, from an ontology engineering point of view, thesauri and other library classification are called light ontologies, in contrast to true ontologies (Daconta et al., 2003; 157; Lassila, O. y McGuinness, D. L., 2001; Gruninger y Uschold, 2002). 

 

References
  • ZENG, M.L. & CHAN, L.M. (2004) Trends and issues in establishing interoperability among knowledge organization systems. Journal of the American Society for Information Science and Technology, 55 (5), pp. 377-395.
  • LASSILA, Ora y MCGUINNESS, Deborah. The Role of Frame-Based Representation on the Semantic Web. KSL Tech Report Number KSL-01-02. Enero de 2001. <http://www.ksl.stanford.edu/people/dlm/etai/lassila-mcguinness-fbr-sw.html >[28/09/2009]
  • GRUNINGER, M. y USCHOLD, M.. Ontologies and Semantic Integration to appear in Software Agents for the Warfighter, the first in a series of reports sponsored by the US Government Information Technology Assessment Consortium (ITAC). Edited by Jeff Bradshaw, Institute for Human and Machine Cognition (IHMC), University of West Florida. 2002.
  • DACONTA, Michael C.; OBRST, Leo J. y  SMITH,  Kevin T. The Semantic Web. A guide to the future of XML, Web Services, and Knowledge Management. Indianapolis: Wiley, 2003.
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Jorge Morato (5/11/2009)
 
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