Topic Maps

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Topic Maps is an abstract model and an associated SGML - or XML -based data format for the formulation of knowledge structures . Topic Maps were standardized in 1999 as the ISO standard ISO / IEC 13250 and later formulated as XML Topic Maps (XTM) in XML.

A topic map is used to collect knowledge about subjects , i.e. objects or topics of the description. Topics can be anything, such as: B. People, places, events. A distinction is made between addressable subjects (that is everything that can be stored in a computer) and non-addressable subjects (everything that can not be stored in a computer, e.g. you, who are reading this page, the idea of Freedom etc.). Objects or topics themselves should and often cannot be changed if knowledge is gathered about them. Therefore Topics used the objects described in a topic map represent . Associations are also part of a topic mapthat describe the links between topics and occurrences that describe topics with documents e.g. B. connect in the WWW . One speaks therefore of the TAO (T for topics, A for associations and O for occurrences) of the topic maps. There are also names and roles ( designations of the function of a topic in an association). A topic can contain several names, which are then assigned to a scope . In this way, a topic can have several names (e.g. in different languages). Conversely, several topics can also have the same name in different scopes . A topic that stands for the sun can e.g. B. have three names ( Sonne , sun , soleil ). If the scope is English , only the name sun is valid, etc. By simply setting the correct scope, a topic map can appear in different languages. Another example: The name duck can stand for an animal, a car or a faulty newspaper report. There can therefore be three topics that have this name. In the first case the scope could be wildlife, in the second motor vehicles , in the third journalism .

Topic Maps are based on the index and thesaurus structures that have been tried and tested in human knowledge processing and develop them further for computerized needs. This distinguishes them from other approaches that start from the background of a formalization that is understandable for the computer , see e.g. B. Resource Description Framework (RDF) and Web Ontology Language (OWL) . Due to their origin, topic maps often avoid inoperable requirements for the knowledge specification that have been defined with a view to inference machines.

Topic maps are intended to enable better navigation and search in Internet resources and other documents and serve to exchange metadata . They have their roots in glossaries , classification systems (for example the subject order of the Open Directory Project ) and thesauri , but their expressiveness goes beyond these. Topic maps can be used to formulate ontologies that can be mapped for the semantic web and also on RDF. There are proposals to implement topic maps with RDF and OWL (Cregan 2005).

The Tolog Query Engine, for example, is a system that enables new knowledge to be derived from a Prolog-type query.

In practice, topic maps are often only used to model simple (faceted) classifications , so that a simplified subset was created independently using the eXchangeable Faceted Metadata Language ( XFML ).

TMAPI exists as a unified API for editing topic maps.

See also

literature

  • Lutz Maicher and Lars Marius Garshol (eds.): Scaling Topic Maps , Proceedings of TMRA 2007. Springer, 2008 ISBN 978-3-540-70873-5
  • Lutz Maicher, Alexander Sigel, and Lars Marius Garshol (eds.): Leveraging the Semantics of Topic Maps , Proceedings of TMRA 2006. Springer, 2007 ISBN 978-3-540-71944-1
  • Lutz Maicher and Jack Park (eds.): Charting the Topic Maps Research and Applications Landscape , Proceedings of TMRA 2005. Springer, 2006, ISBN 3-540-32527-1
  • Jack Park and Sam Hunting: XML Topic Maps: Creating and Using Topic Maps for the Web. Addison-Wesley, 2002, ISBN 0-201-74960-2 (in bibliography )
  • Thomas Schwotzer: A P2P system based on topic maps to support knowledge flows , Vdm Verlag Dr. Müller, April 2008, ISBN 978-3-639-00837-1 ( web link )
  • Stefan Smolnik: Knowledge Management with Topic Maps in Collaborative Environments - Identification, Explication and Visualization of Semantic Networks in Organizational Memories Shaker, Aachen, February 2006, ISBN 3-8322-4494-8
  • Richard Widhalm and Thomas Mück: Topic Maps: Semantic Search on the Internet. Springer Verlag, March 2002, ISBN 3-540-41719-2

Web links

Commons : Topic Maps  - collection of images, videos and audio files