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Lightweight Ontologies

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Theory and Applications of Ontology: Computer Applications

Abstract

In this chapter, we discuss the notion of lightweight ontologies. First defining what we mean by ontology in our sense, we proceed to relate ontology to other related knowledge organisation structures, formal and less formal. We then consider the notion of lightweight ontology, principally based on the expressivity of the ontology description, rather than other possible aspects such as scope, depth or computational tractability, though as we discuss, these other aspects are not entirely unrelated. We then look at ontologies and the semantic web, the emergence of which over the past 10–15 years has seen increased interest in ontologies and associated topics. We proceed to examine 3 key areas where ontologies are being used in IT systems today and briefly discuss the centrality of ontologies and semantic technology to computer science in the future.

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Notes

  1. 1.

    We define an entity to be that which has a distinct separate existence.

  2. 2.

    Note that while advocating a logic-based approach we are not advocating any specific logic (such as description logic, for example). Indeed, currently available logics lack some of the properties which would seem to be required for representing and reasoning at the scale and lack of precision found on the web. [Fensel and van Harmelen 2007] discuss new, more appropriate inference mechanisms.

  3. 3.

    http://www.cs.vassar.edu/faculty/welty/aaai-99/

  4. 4.

    http://www.oreillynet.com/pub/a/oreilly/tim/news/2005/09/30/what-is-web-20.html

  5. 5.

    Taxonomy is also sometimes used loosely (and incorrectly) to denote any set of categories against which electronic content has been classified.

  6. 6.

    Though note that we do not necessarily exclude non-logical but formal accounts (e.g. in the case where the knowledge is probabilistic in nature).

  7. 7.

    The OWL web ontology language, for example, allows a set of ground facts to be defined as an ontology. Strictly speaking, this is inadmissible.

  8. 8.

    ‘Deeper’ in the sense more elaborate and offering a more precise model of the domain at hand.

  9. 9.

    http://www.flickr.com/

  10. 10.

    http://del.icio.us/

  11. 11.

    http://www.dmoz.org/

  12. 12.

    http://www.oclc.org/dewey/

  13. 13.

    http://www.w3c.org/

  14. 14.

    http://www.w3.org/2000/Talks/1206-xml2k-tbl/slide1-0.html

  15. 15.

    Strictly, RDF and its sister language RDF Schema

  16. 16.

    http://www.w3.org/TR/owl-guide/

  17. 17.

    http://www.ontoprise.de/

  18. 18.

    Wordnet is a lexical reference system in which English nouns, verbs, adjectives and adverbs are organised into synonym sets, with relations linking the sets. Wordnet provides both a web-based user interface for the casual user and also a programming interface to enable incorporation into other systems, e.g. software for mapping between different terminologies.

  19. 19.

    In general a graph, but frequently a tree where the product classification is organised as a strict hierarchy.

  20. 20.

    It is not our intention to discuss the definition of knowledge management here but a broad definition could be “the management by an organisation of its intellectual assets to deliver more efficient and effective ways of working.” Better access to and management of unstructured information is a key part of this endeavour.

  21. 21.

    Out of a total of 143,726 queries submitted to the search engine, there were 251,192 occasions where a user clicked to view more than the first page of results. Ten results per page are returned by default.

  22. 22.

    Statistical techniques employ algorithms with well-defined mathematical properties, usually derived based on certain assumptions about the datasets being used. Machine learning techniques are generally heuristic techniques with no formally derived mathematical properties, e.g. iterative techniques for which no proof of convergence exists. The two approaches may suit different circumstances, or can be used together in a complementary fashion.

  23. 23.

    The use of pronouns and other short words in place of longer words or phrases is called anaphora. Hence, the matching of such short words with their longer equivalent is called anaphora resolution.

  24. 24.

    http://proton.semanticweb.org/

  25. 25.

    http://lucene.apache.org/

  26. 26.

    http://www.w3.org/TR/rdf-sparql-query/

  27. 27.

    http://www.w3.org/Submission/SWRL/

  28. 28.

    Gartner Group, 2004.

  29. 29.

    Operational Support Systems: systems that support the daily operation of an organisation’s business including, for example, billing, ordering, delivery, customer support.

  30. 30.

    See, for example, http://www.findarticles.com/p/articles/mi_m0TLC/is_5_36/ai_86708476

  31. 31.

    http://www.opencalais.com/

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Acknowledgments

Céline van Damme is thanked for insightful discussions about folksonomies and informal ontolgoies in the enterprise. Section 9.4 is based partly on Warren and Davies (2007), while Section 9.6 is based partly on Davies et al. (2006).

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  1. BT Innovate, British Telecommunications plc, London, UK

    John Davies

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Editors and Affiliations

  1. Dipto. Sociologia e Ricerca Sociale, Universita di Trento, Via Verdi 26, Trento, 38100, Italy

    Roberto Poli

  2. 23rd Place NE., 13544, Seattle, 98125-3325, Washington, USA

    Michael Healy

  3. Computer Technology Institute, Hellenic Open University &, N. Kazantzaki Str., Patras, 265 00, Greece

    Achilles Kameas

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Davies, J. (2010). Lightweight Ontologies. In: Poli, R., Healy, M., Kameas, A. (eds) Theory and Applications of Ontology: Computer Applications. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-8847-5_9

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