Advertisement

Semantic Information Retrieval Dedicated to Multimedia Systems: A Platform Based on Conceptual Graphs

  • Xavier Aimé
  • Francky Trichet
Part of the Studies in Computational Intelligence book series (SCI, volume 142)

Abstract

OSIRIS is a web platform dedicated to the development of Ontology-based System for Semantic Information Retrieval and Indexation of multimedia resources which are shared within communautary and open web Spaces. Based on the use of both heavyweight ontologies and thesaurii, OSIRIS allows the end-user (1) to describe the semantic content of its resources by using an intuitive natural-language based model of annotation which is founded on the triple (Subject, Verb, Object), and (2) to formally represent these annotations by using Conceptual Graphs. Moreover, each resource can be described by adopting multiple points of view, which usually correspond to different end-users. These different points of view can be defined by using multiple ontologies which can be related to connected (or not-connected) domains. Developed from the integration of Semantic Web technologies and Web 2.0 technologies, OSIRIS aims at facilitating the deployment of semantic, collaborative, communautary and open web spaces.

Keywords

ontology heavyweight ontology thesaurus semantic annotation semantic informa-tion retrieval conceptual graphs semantic web intelligent multimedia system collaborative annotation social tagging semantic web 2.0 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bechhofer, S., Volz, R., Lord, P.: Cooking the Semantic Web with the OWL-API. In: Fensel, D., Sycara, K.P., Mylopoulos, J. (eds.) ISWC 2003. LNCS, vol. 2870, pp. 659–675. Springer, Heidelberg (2003)Google Scholar
  2. 2.
    Bocconi, S., Nack, F., Hardman, L.: Supporting the Generation of Argument Structure within Video Sequence. In: Proceedings of the Sixteenth ACM Conference on Hypertext and Hypermedia, pp. 75–84 (2005)Google Scholar
  3. 3.
    Chein, M., Mugnier, M.L.: Conceptual Graphs: fundamental notions. Revue d’Intelligence Artificielle (RIA) 6(4), 365–406 (1992) HermèsGoogle Scholar
  4. 4.
    Crampes, M., Ranwez, S.: Ontology-Supported and Ontology-Driven Conceptual Navigation on the World Wide Web. In: Proceedings of the eleventh ACM on Hypertext and hypermedia, pp. 191–199 (2000)Google Scholar
  5. 5.
    Euzenat, J., Shvaiko, P.: Ontology Matching, p. 341. Springer, Heidelberg (2007)MATHGoogle Scholar
  6. 6.
    Fürst, F., Trichet, F.: Heavyweight Ontology Engineering. In: Meersman, R., Tari, Z., Herrero, P. (eds.) OTM 2006 Workshops. LNCS, vol. 4277, pp. 38–39. Springer, Heidelberg (2006a)CrossRefGoogle Scholar
  7. 7.
    Fürst, F., Trichet, F.: Reasoning on the Semantic Web needs to reason both on ontology-based assertions and on ontologies themselves. In: Proceedings of the International Workshop on Reasoning on the Web (Row 2006), Co-located with the 15th International World Wide Web Conference (WWW 2006, Edinburgh) (2006b)Google Scholar
  8. 8.
    Fürst, F., Leclere, M., Trichet, F.: Operationalizing domain ontologies: a method and a tool. In: Proceedings of the 16th European Conference on Artificial Intelligence (ECAI 2004), pp. 318–322. IOS Press, Amsterdam (2004)Google Scholar
  9. 9.
    Gomez-Perez, A., Fernandez-Lopez, M.: Ontological Engineering. In: Advanced Information and Knowledge Processing (2003)Google Scholar
  10. 10.
    Genest, D., Salvat, E.: A Platform allowing typed nested graphs: how CoGITo became CoGITaNT. In: Mugnier, M.-L., Chein, M. (eds.) ICCS 1998. LNCS (LNAI), vol. 1453, pp. 154–161. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  11. 11.
    Greaves, M.: Semantic Web 2.0. IEEE Intelligent Systems 22(2), 94–96 (2007)CrossRefGoogle Scholar
  12. 12.
    Shvaiko, P., Euzenat, J.: A Survey of Schema-based Matching Approaches. In: Spaccapietra, S. (ed.) Journal on Data Semantics IV. LNCS, vol. 3730, pp. 146–171. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  13. 13.
    Sowa, J.: Conceptual Structures: information processing in mind and machine, Handbook. Addison-Wesley, Reading (1984)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Xavier Aimé
    • 1
  • Francky Trichet
    • 1
  1. 1.LINA, Laboratoire d’Informatique de Nantes Atlantique (UMR-CNRS 6241)University of Nantes - Team Knowledge and Decision (KOD)Nantes cedex 03France

Personalised recommendations