Search
  • All Journals
Advanced Search
Search articles by Title, Author, Keywords, DOI
7.7CiteScore
2.0Impact Factor
Advanced Search
MAP  >  The Knowledge Engineering Review
2013 Volume 28
Article Contents
Next Previous
RESEARCH ARTICLE   Open Access    

Visual reasoning with graph-based mechanisms: the good, the better and the best

  • 161 rue ADA

More Information
  • Abstract: This paper presents a graph-based knowledge representation and reasoning language. This language benefits from an important syntactic operation, which is called a graph homomorphism. This operation is sound and complete with respect to logical deduction. Hence, it is possible to do logical reasoning without using the language of logic but only graphical, thus visual, notions. This paper presents the main knowledge constructs of this language, elementary graph-based reasoning mechanisms, as well as the graph homomorphism, which encompasses all these elementary transformations in one global step. We put our work in context by presenting a concrete semantic annotation application example.
  • 加载中
  • Aissaoui G., Genest D., Loiseau S.2003. Cognitive map of conceptual graphs: a graphical model to help for decision. In ICCS, de Moor, A., Lex, W. & Ganter, B. (eds), Lecture Notes in Computer Science 2746, 337–350 Springer, ISBN 3-540-40576-3.

    Google Scholar

    Baader F., Calvanese D., McGuinness D. L., Nardi D., Patel-Schneider P. F. (eds) 2003. The Description Logic Handbook. Cambridge University Press.

    Google Scholar

    Baget J. F.2005. RDF entailment as a graph homomorphism. In Proceedings of ISWC'05. Galway, Ireland.

    Google Scholar

    Bos C., Botella B., Vanheeghe P.1997. Modeling and simulating human behaviors with conceptual graphs. In Proceedings of ICCS'97, Lecture Notes in Artificial Intelligence 1257, 275–289. Springer.

    Google Scholar

    Carloni O., Leclère M., Mugnier M.-L.2006. Introducing graph-based reasoning into a knowledge management tool: an industrial case study. In Proceedings of IEA/AIE. 590–599. Annecy, France.

    Google Scholar

    Chauvin L., Genest D., Loiseau S.2008. Contextual cognitive map. In ICCS, Eklund, P. W. & Haemmerlé, O. (eds), Lecture Notes in Computer Science 5113, 231–241, Springer. ISBN 978-3-540-70595-6.

    Google Scholar

    Chein M., Mugnier M.-L.1992. Conceptual graphs: fundamental notions. Revue d'Intelligence Artificielle60(4), 365–406.

    Google Scholar

    Chein M., Mugnier M.2009. Graph-based Knowledge Representation: Computational Foundations of Conceptual Graphs. Springer.

    Google Scholar

    Genest D., Chein M.2005. A Content-search information retrieval process based on conceptual graphs. Knowledge and Information Systems (KAIS)8, 292–309.

    Google Scholar

    Hayes P.2004. RDF Semantics. W3C Recommendation. http://www.w3.org/TR/2004/REC-rdf-mt-20040210/.

    Google Scholar

    Horst H.2004. Extending the RDFS entailment lemma. In Proceedings of the 3rd International Semantic Web Conference ISWC'04, Sheila A. McIlraith, Dimitris Plexousakis & Frank van Harmelen (eds). Lecture Notes in Computer Science 3298, 77–91. Springer.

    Google Scholar

    Lalande S., Staykova K., Chein M., Gutierrez A., Saraydarova V., Dochev D.2009. Using domain knowledge to speed up the annotation of digital content with conceptual graphs. Cybernetics and Information Technology90(3), 22–38.

    Google Scholar

    Lehman F.1992. Semantics Networks in Artificial Intelligence. Pergamon Press.

    Google Scholar

    Masterman M.1962. Semantic message detection for machine translation, using an interlingua. In International Conference on Machine Translation of Languages and Applied Language Analysis, 438–475.

    Google Scholar

    Moreau N., Leclère M., Chein M., Gutierrez A.2007. Formal and graphical annotations for digital objects. In Proceedings of the International Workshop on Semantically Aware Document Processing and Indexing (SADPI'07), ACM Digital Library, ACM International Conference Proceeding Series259, 69–78. ACM.

    Google Scholar

    Novak J., Canas A.2006. The origins of the concept mapping tool and the continuing evolution of the tool. Information Visualization Journal5, 175–184.

    Google Scholar

    Raimbault T., Genest D., Loiseau S.2005. A new method to interrogate and check uml class diagrams. In ICCS, Dau, F., Mugnier, M.-L. & Stumme, G. (eds), Lecture Notes in Computer Science 3596, 353–366. Springer, ISBN 3-540-27783-8.

    Google Scholar

    Richens R.1956. Programming for mechanical translation. Mechanical Translation3.

    Google Scholar

    Shapiro S.1979. The sneps semantic network processing system. In The Representation and Use of Knowledge by Computers, Findler, N.V. (ed.). Academic Press, New York, 179–203.

    Google Scholar

    Shapiro S.2000. Sneps: A logic for natural language understanding and commonsense reasoning. In Natural Language Processing and Knowledge Representation: Language for Knowledge and Knowledge for Language, Lucja Iwanska & Stuart C. Shapiro (eds), 175–195.

    Google Scholar

    Shaw M., Gaines B.1995. Knowledge and requirements engineering. In Proceedings of the 9th Banff Knowledge Accquisition For Knowledge-Based Systems Workshop.

    Google Scholar

    Sowa J. F.1976. Conceptual graphs. IBM Journal of Research and Development20(4), 336–375.

    Google Scholar

    Sowa J. F.1984. Conceptual Structures: Information Processing in Mind and Machine. Addison-Wesley.

    Google Scholar

    Woods W., Schmolze J.1992. The kl-one family. Computers Mathematical Applications23, 133–177.

    Google Scholar

  • Cite this article

    Michel Chein, Marie-Laure Mugnier, Madalina Croitoru. 2013. Visual reasoning with graph-based mechanisms: the good, the better and the best. The Knowledge Engineering Review 28(3)249−271, doi: 10.1017/S0269888913000234
    Michel Chein, Marie-Laure Mugnier, Madalina Croitoru. 2013. Visual reasoning with graph-based mechanisms: the good, the better and the best. The Knowledge Engineering Review 28(3)249−271, doi: 10.1017/S0269888913000234
    shu
Download PDF

Article Metrics

Article views(26) PDF downloads(96)

Other Articles By Authors

RESEARCH ARTICLE   Open Access    

Visual reasoning with graph-based mechanisms: the good, the better and the best

  • 161 rue ADA

  • Published online: 07 August 2013
The Knowledge Engineering Review  28,  2013, 28(3): 249−271  |  Cite this article

Abstract: Abstract: This paper presents a graph-based knowledge representation and reasoning language. This language benefits from an important syntactic operation, which is called a graph homomorphism. This operation is sound and complete with respect to logical deduction. Hence, it is possible to do logical reasoning without using the language of logic but only graphical, thus visual, notions. This paper presents the main knowledge constructs of this language, elementary graph-based reasoning mechanisms, as well as the graph homomorphism, which encompasses all these elementary transformations in one global step. We put our work in context by presenting a concrete semantic annotation application example.

    HTML

    • http://www.lirmm.fr/cogui/

    • http://cogitant.sourceforge.net/

    • http://www.w3.org/TR/REC-rdf-syntax/

    Rights and permissions
    • Copyright © Cambridge University Press 2013 2013Cambridge University Press
References (24)
  • About this article
    Cite this article
    Michel Chein, Marie-Laure Mugnier, Madalina Croitoru. 2013. Visual reasoning with graph-based mechanisms: the good, the better and the best. The Knowledge Engineering Review 28(3)249−271, doi: 10.1017/S0269888913000234
    Michel Chein, Marie-Laure Mugnier, Madalina Croitoru. 2013. Visual reasoning with graph-based mechanisms: the good, the better and the best. The Knowledge Engineering Review 28(3)249−271, doi: 10.1017/S0269888913000234
    shu

    • Catalog

      /

      DownLoad:  Full-Size Img  PowerPoint
      Return
      Return