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Graph-Based Representation and Reasoning for Ontologies

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Computational Intelligence: A Compendium

Part of the book series: Studies in Computational Intelligence ((SCI,volume 115))

An ontology, in the Knowledge Engineering and Artificial Intelligence sense, is a framework for the domain knowledge of an intelligent system. An ontology structures the knowledge, and acts as a container for the knowledge. We define knowledge conjunction as one or more agents using multiple ontologies to perform tasks and understand the domain. Once a common ontology is agreed upon, the agents then have a common background in which to share knowledge. No current method exists that allows intelligent agents to agree on a common framework for sharing knowledge, although there has been some work in comparing semantic meanings within an ontology [44]. This means that agents are unable to use the knowledge of another agent, as the knowledge is meaningless if it isn’t presented in a proper context or a common ‘language’.

In this Chapter, we first give an overview of Conceptual Graph Theory, including what conceptual graphs are and how they work. We then take a different point-of-view for the representation of ontologies. Rather than constructing a CG to represent the ontology, we assert that the CG formalism is better exploited by using a combination of the concept type hierarchy, the canonical formation rules, the conformity relation and subsumption to act as the framework for the knowledge base. An unpopulated ontology (which is simply a framework for the knowledge) is represented by the type hierarchy without specific individuals, while the populated ontology (the framework, as well as the knowledge of the domain) is represented by a hierarchy and the specific conceptual graphs which instantiate individuals, constraints, situations or concepts.

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References

  1. Aït-Kaci H  (1986) An algebraic semantics approach to the effective resolution of type quations. Theoretical Computer Science, 45(3): 293-351.

    Article  MATH  MathSciNet  Google Scholar 

  2. Aït-Kaci H, Nasr R (1986) LOGIN: a logic programming language with built-in inheritance. J. Logic Programming, 3(3): 185-215.

    Article  Google Scholar 

  3. Amati G, Ounis I (2000) Conceptual graphs and first order logic. The Computer J., 431: 1-12.

    Article  MATH  Google Scholar 

  4. Baader F, Siekmann J (1994) Unification theory. In: Gabbay DM, Hogger CJ, Robinson JA (eds.) Handbook of Logic in Artificial Intelligence and Logic Programming. Clarendon Press, Oxford, UK, 2: 41-126.

    Google Scholar 

  5. Bechhofer S, Carr L, Goble C, Kampa S, Miles-Board T (2002) The semantics of semantic annotation. In: Meersman R, Tari Z (eds.) Proc. Intl. Conf. Ontologies, Databases and Semantics, 30 October - 1 November, Irvine, CA, Springer, New York, NY: 1152-1167.

    Google Scholar 

  6. Cao TH, Creasy PN, Wuwongse (1997) Fuzzy unification and resolution proof procedure for fuzzy conceptual graph programs. In: Lukose D, Delugach HS, Keeler M, Searle L, Sowa JF (eds.) Proc. 5th Intl. Conf. Conceptual Structures, August, Seattle, WA, Springer, New York, NY: 386-400.

    Google Scholar 

  7. Carpenter B(1992) The Logic of Typed Feature Structures. Cambridge University Press, Cambridge, UK.

    Book  MATH  Google Scholar 

  8. Chein M, Mugnier M-L (1992) Conceptual graphs: fundamental notions. Revue d’Intelligence Artificielle, 64: 365-406.

    Google Scholar 

  9. Cobb EE (2002) Will web services cause the widespread adoption of the Internet by business? In: Meersman R, Tari Z (eds.) Proc. 1st Intl. Conf. Ontologies, Databases, and Application of Semantics, 30 October - 1 November, Irvine, CA, IEEE Press, Piscataway, NJ.

    Google Scholar 

  10. Cogis O, Guinaldo O (1995) A linear descriptor for conceptual graphs and a class for polynomial isomorphism test. In: Ellis R, Levison R, Rich W, Sowa JF (eds.) Proc. 3rd Intl. Conf. Conceptual Structures, August, Santa Cruz, CA, Springer-Verlag, New York, NY: 263-277.

    Google Scholar 

  11. Corbett DR (2001) Conceptual graphs with constrained reasoning. Revue d’Intelligence Artificielle, 151: 87-116.

    Google Scholar 

  12. Corbett DR (2001) Reasoning with conceptual graphs. In: Stumptner M, Corbett D, Brooks MJ (eds.) Proc. 14th Australian Joint Conf. Artificial Intelli-gence, December, Adelaide, South Australia, Lecture Notes in Computer Science 2256, Springer-Verlag, Berlin.

    Google Scholar 

  13. Corbett DR (2002) Reasoning with ontologies by using knowledge conjunction in conceptual raphs. In: Meersman R, Tari Z (eds.) Proc. Intl. Conf. Ontologies, Databases and Applications of Semantics, October, Irvine, CA, Springer, New York, NY: 1304-1316.

    Google Scholar 

  14. Corbett DR (2003) Reasoning and Unification over Conceptual Graphs. Kluwer Academic Publishers, New York, NY.

    MATH  Google Scholar 

  15. Corbett DR, Woodbury RF (1999) Unification over constraints in conceptual graphs. In: Tepfenhart WM, Cyre WR (eds.) Proc. 7th Intl. Conf. Concep-tual Structures, July, Blacksburg, VA, Lecture Notes in Computer Science 1640, Springer-Verlag, New York, NY: 470-479.

    Google Scholar 

  16. Davey BA, Priestley HA (1990) Introduction to Lattices and Order. Cambridge University Press, Cambridge, UK.

    MATH  Google Scholar 

  17. Fikes R, McGuinness DL (2001) An axiomatic semantics for RDF, RDF schema, and DAML+OIL Knowledge Systems Laboratory, Stanford University, Palo Alto, CA.

    Google Scholar 

  18. Franconi E (2004) Using ontologies. IEEE Intelligent Systems, 191: 73-74.

    Google Scholar 

  19. Heymans S, Vermeir D (2002) A defeasible ontology language. In: Meersman R, Tari Z (eds.) Proc. Intl. Conf. Ontologies, Database and Applications of Semantics, October, Irvine, CA, Springer-Verlag, New York, NY: 1033-1046.

    Google Scholar 

  20. Knight K (1989) Unification: a multidisciplinary survey. ACM Computing Surveys, 211: 93-124.

    Article  MATH  MathSciNet  Google Scholar 

  21. Leclère M (1997) Reasoning with type definitions. In: Lukose D, Delugach HS, Keeler M, Searle L, Sowa JF (eds.) Proc. 5th Intl. Conf. Conceptual Structures, August, Seattle, WA, Springer-Verlag, New York, NY: 401-414.

    Google Scholar 

  22. Lehmann F(1992) Semantic networks. Computers & Mathematics with Applications, 23(2-5): 1-50.

    Article  MATH  MathSciNet  Google Scholar 

  23. McGuinness DL (2003) Ontologies come of age. In: Fensel D, Hendler J, Lieber-man H, Wahlster W (eds.) Spinning the Semantic Web: Bringing the World Wide Web to Its Full Potential. MIT Press, Cambridge, MA: 171-197.

    Google Scholar 

  24. McGuinness DL, Fikes R, Hendler J, Stein LA (2002) DAML+OIL: an ontology language for the semantic web. IEEE Intelligent Systems, 175: 72-80.

    Article  Google Scholar 

  25. McGuinness DL, Fikes R, Rice J, Wilder S (2000) The Chimaera ontology environment. In: Kautz H, Porter B (eds.) Proc. 17th National Conf. Artifi-cial Intelligence, July, Austin, TX, AAAI Press/MIT Press, Cambridge, MA: 1123-1124.

    Google Scholar 

  26. Mitchard H(1998) Cognitive model of an operations officer. PhD Thesis, Department of Computer and Information Science, University of South Australia, Adelaide.

    Google Scholar 

  27. Mitchard H, Winkles J, Corbett DR (2000) Development and evaluation of a cognitive model of an Air Defence Operations Officer. In: Davis C, van der Gelder TJ, Wales R (eds.) Proc. 5th Biennial Conf. Australasian Cog-nitive Science Society, May, Adelaide, South Australia, Springer-Verlag, Berlin: 479-492.

    Google Scholar 

  28. Mugnier M-L, Chein M (1996) Représenter des connaissances et raisonner avec des graphes. Revue d’Intelligence Artificielle, 106: 7-56.

    MATH  Google Scholar 

  29. Müller T (1997) Conceptual Graphs as Terms: Prospects for Resolution The-orem Proving. Technical Report TR97-01, Department of Computer Science, Vrije Universiteit, Amsterdam, The Netherlands.

    Google Scholar 

  30. Nguyen P, Corbett D (2006) A basic mathematical framework for conceptual graphs. IEEE Trans. Knowledge and Data Engineering, 182: 261-271.

    Article  Google Scholar 

  31. Older WJ (1997) Involution narrowing algebra. Constraints, 2: 113-130.

    Article  MATH  MathSciNet  Google Scholar 

  32. Reynolds JC (1970) Transformational systems and the algebraic structure of atomic ormulas. Machine Intelligence, 5: 153-163.

    Google Scholar 

  33. Smith MK (2002) Web Ontology Issues W3C,5.10(available online at http://www.w3.org/2001/sw/WebOnt/webont-issues.html.- last accessed9 July, 2007).

  34. Sowa JF (1984) Conceptual Structures: Information Processing in Mind and Machine. Addison Wesley, Reading, MA.

    MATH  Google Scholar 

  35. Sowa JF (1992) Conceptual Graphs Summary. Conceptual Structures: Current Research and Practice. Ellis Horwood, Chichester, UK.

    Google Scholar 

  36. Sowa JF (1999) Conceptual graphs: draft proposed American National Stan-dard. In: Tepfenhart WM, Cyre WR (eds.) Proc. 7th Intl. Conf. Conceptual Structures, July, Blacksburg, VA, Springer-Verlag, New York, NY: 1-65.

    Google Scholar 

  37. van Harmelen F, Hendler J, Horrocks I, McGuinness DL, Patel-Schneider PF, Stein LA (2004) OWL Web Ontology Language Reference (available online at http://www.w3.org/TR/owl-ref/- last accessed 9 July 2007).

  38. Wermelinger M, Lopes JG (1994) Basic conceptual structures theory. In: Tepfenhart W, Cyre W (eds.) Proc. 2nd Intl. Conf. Conceptual Structures, August, College Park, Maryland, Springer-Verlag, New York, NY: 144-159.

    Google Scholar 

  39. Wille R (1996) Conceptual structures of multicontexts. In: Eklund P, Ellis G, Mann G (eds.) Proc. 4th Intl. Conf. Conceptual Structures, August, Sydney, Australia, Springer-Verlag, Berlin: 23-29.

    Google Scholar 

  40. Wille R (1996) Short introduction to formal concept analysis. In: Eklund P, Ellis G, Mann G (eds.) Proc. Intl. Conf. Conceptual Structures, August, Sydney, Australia, Springer-Verlag, Berlin: 1-22.

    Google Scholar 

  41. Willems M (1995) Projection and unification for conceptual graphs. In: Ellis, Levinson, Rich, Cruz (eds.) Proc. 3rd Intl. Conf. Conceptual Structures, August, Santa Cruz, CA, Springer-Verlag, New York, NY: 278-292.

    Google Scholar 

  42. Woodbury R, Datta S, Burrow AL (2000) Erasure in design space exploration. In: Gero JS (ed.) Proc. 6th Intl. Artificial Intelligence in Design Conf., June, Worcester, MA, Kluwer, New York, NY: 521-531.

    Google Scholar 

  43. Yang G, Kifer M (2002) On the semantics of anonymous identity and reificaion. In: Meersman R, Tari Z (eds.) Proc. 1st Intl. Conf. Ontologies, Databases and Applications of Semantics, 30 October - 1 November, Irvine, CA, Springer-Verlag, New York, NY: 1047-1966.

    Google Scholar 

  44. Yang G, Kifer M (2002) Well-founded optimism: inheritance in frame-based knowledge bases. In: Meersman R, Tari Z (eds.) Proc. Intl. Conf. Ontologies, Databases and Applications of Semantics, 30 October - 1 November, Irvine, CA, Springer-Verlag, New York, NY: 1013-1032.

    Google Scholar 

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Corbett, D.R. (2008). Graph-Based Representation and Reasoning for Ontologies. In: Fulcher, J., Jain, L.C. (eds) Computational Intelligence: A Compendium. Studies in Computational Intelligence, vol 115. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78293-3_8

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  • DOI: https://doi.org/10.1007/978-3-540-78293-3_8

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