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Knowledge Engineering

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Abstract

Knowledge engineering refers to all technical, scientific and social aspects involved in designing, maintaining and using knowledge-based systems. Research in this domain requires to develop studies on the nature of the knowledge and its representation, either the users’ knowledge or the knowledge-based system’s knowledge. It also requires the analysis of what type of knowledge sources is considered, what human-machine interaction is envisaged and more generally the specific end use. To that end, knowledge engineering needs to integrate innovation originating from artificial intelligence, knowledge representation, software engineering as well as modelling. This integration enables both users and software systems to manage and use the knowledge for inference reasoning. Other advances are fuelling new methods, software tools and interfaces to support knowledge modelling that are enabled by conceptual or formal knowledge representation languages. This chapter provides an overview of the main issues and major results that are considered as milestones in the domain, with a focus on recent advances marked by the raise of the semantic web, of ontologies and the social web.

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Fig. 1
Fig. 2

Notes

  1. 1.

    Knowledge acquisition refers to the process of gathering expert knowledge (called “knowledge mining” at that time) and representing it in the form of rules and facts in the hope that the KBS behaves like the expert would in a similar situation. The difficulty to precisely collect or capture this knowledge, which is implicit and hard to elicit in many ways, reduces the amount and quality of knowledge actually represented, as the term “bottleneck” illustrates.

  2. 2.

    Whereas the KE English-speaking community uses “lexical ontology”, many French research groups refer to Termino-Ontological Resource (TOR) (Reymonet et al. 2007) for very similar knowledge structures.

  3. 3.

    http://www.jfsowa.com/ontology/toplevel.htm.

  4. 4.

    http://www.ontologyportal.org/.

  5. 5.

    http://www.loa-cnr.it/DOLCE.html.

  6. 6.

    http://suo.ieee.org/.

  7. 7.

    http://ontolog.cim3.net/cgi-bin/wiki.pl?UpperOntologySummit/UosJointCommunique.

  8. 8.

    http://www.onto-med.de/ontologies/gfo-bio/index.jsp.

  9. 9.

    http://www.estrellaproject.org/lkif-core/.

  10. 10.

    For a historical outline on knowledge-based system, one can read Aussenac (1989), Stefik (1995), Aussenac-Gilles et al. (1996), or Charlet et al. (2000).

  11. 11.

    http://www.commonkads.uva.nl/.

  12. 12.

    http://wordnet.princeton.edu/wordnet/.

  13. 13.

    http://gate.ac.uk/.

  14. 14.

    http://www.nooj4nlp.net/.

  15. 15.

    http://www-igm.univ-mlv.fr/~unitex/.

  16. 16.

    http://www.ontotext.com/kim/.

  17. 17.

    http://linguastream.org/.

  18. 18.

    http://domino.research.ibm.com/comm/research_projects.nsf/pages/uima.index.html.

  19. 19.

    http://www.neon-toolkit.org/.

  20. 20.

    For a survey of the main existing methodologies, see Fernández-López and Gómez-Pérez (2002).

  21. 21.

    http://protege.stanford.edu/.

  22. 22.

    http://code.google.com/p/swoop/.

  23. 23.

    http://www.hozo.jp/ckc07demo/.

  24. 24.

    http://www.ims.uni-stuttgart.de/projekte/corplex/TreeTagger/.

  25. 25.

    http://www.neon-toolkit.org/wiki/Neon_Plugins.

  26. 26.

    http://lipn.univ-paris13.fr/terminae/.

  27. 27.

    http://kmi-web05.open.ac.uk/WatsonWUI/.

  28. 28.

    http://swoogle.umbc.edu/.

  29. 29.

    http://asaha.com/ebook/wNjE3MzI-/OntoSearch--An-Ontology-Search-Engine.pdf.

  30. 30.

    Referred to as Ontology Design Pattern or ODP.

  31. 31.

    http://ontologydesignpatterns.org/wiki/Main_Page.

  32. 32.

    https://github.com/lmazuel/onagui.

  33. 33.

    http://www.w3.org/TR/daml+oil-reference.

  34. 34.

    http://www.daml.org/.

  35. 35.

    https://www.w3.org/TR/owl2-new-features/#F15:_OWL_2_EL.2C_OWL_2_QL.2C_OWL_2_RL.

  36. 36.

    https://www.w3.org/RDF/.

  37. 37.

    https://www.w3.org/TR/rdf-schema/.

  38. 38.

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

  39. 39.

    https://www.w3.org/TR/rif-overview/.

  40. 40.

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

  41. 41.

    http://logic.aifb.uni-karlsruhe.de/wiki/DLP.

  42. 42.

    https://www.w3.org/TR/2009/REC-skos-reference-20090818/.

  43. 43.

    http://www.3mtcs.com/resources/hl7cts.

  44. 44.

    http://cordis.europa.eu/ist/kct/knowledgeweb_synopsis.htm.

  45. 45.

    http://www.neon-project.org/.

  46. 46.

    I.e. content indexing with user’s metadata. The sets of labels then form folksonomies.

  47. 47.

    http://wiki.dbpedia.org/.

  48. 48.

    https://fr.wikipedia.org.

  49. 49.

    http://www.geonames.org/.

  50. 50.

    https://musicbrainz.org/.

  51. 51.

    https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/research/yago-naga/yago/.

  52. 52.

    http://babelnet.org/.

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Aussenac-Gilles, N., Charlet, J., Reynaud, C. (2020). Knowledge Engineering. In: Marquis, P., Papini, O., Prade, H. (eds) A Guided Tour of Artificial Intelligence Research. Springer, Cham. https://doi.org/10.1007/978-3-030-06164-7_23

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