Learning Meta-descriptions of the FOAF Network

  • Gunnar AAstrand Grimnes
  • Pete Edwards
  • Alun Preece
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3298)


We argue that in a distributed context, such as the Semantic Web, ontology engineers and data creators often cannot control (or even imagine) the possible uses their data or ontologies might have. Therefore ontologies are unlikely to identify every useful or interesting classification possible in a problem domain, for example these might be of a personalised nature and only appropriate for a certain user in a certain context, or they might be of a different granularity than the initial scope of the ontology. We argue that machine learning techniques will be essential within the Semantic Web context to allow these unspecified classifications to be identified. In this paper we explore the application of machine learning methods to FOAF, highlighting the challenges posed by the characteristics of such data. Specifically, we use clustering to identify classes of people and inductive logic programming (ILP) to learn descriptions of these groups. We argue that these descriptions constitute re-usable, first class knowledge that is neither explicitly stated nor deducible from the input data. These new descriptions can be represented as simple OWL class restrictions or more sophisticated descriptions using SWRL. These are then suitable either for incorporation into future versions of ontologies or for on-the-fly use for personalisation tasks.


Resource Description Framework Inductive Logic Programming Conceptual Graph Resource Description Framework Graph Resource Description Framework Schema 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Berners-Lee, T., Hendler, J., Lassila, O.: The semantic web. Scientific American, 28–37 (2001)Google Scholar
  2. 2.
    Brickley, D., Guha, R.V.: Resource Description Framework (RDF) Schema Specification. W3c recommendation,World Wide Web Consortium (2000)Google Scholar
  3. 3.
    McGuinness, D.L., van Harmelen, F.: Web Ontology Language (OWL): Overview. W3c recommendation,World Wide Web Consortium (2003)Google Scholar
  4. 4.
    Lassila, O., Swick, R.R.: Resource Description Framework (RDF) Model and Syntax Specification. W3c recommendation, World Wide Web Consortium (1999)Google Scholar
  5. 5.
    Grimnes, G.A., Edwards, P., Preece, A.: Learning from Semantic Flora and Fauna. In: SemanticWeb Personalization Workshop, AAAI, San Jose (2004)Google Scholar
  6. 6.
    Vorhees, E.: Implementing agglomerative hierarchical clustering algorithms for use in document retrieval. Information Processing & Management 22, 465–476 (1986)CrossRefGoogle Scholar
  7. 7.
    Srinivasan, A.: The Aleph Manual (2001),
  8. 8.
    Horrocks, I., Patel-Scheider, P., Boley, H., Tabet, S., Groshof, B., Dean, M.: SWRL: A Semantic Web Rule Language Combining OWL and RuleML. DARPA DAML Program (2003)Google Scholar
  9. 9.
    Hamming, R.: Error Detecting and Error Correcting Codes. Bell System Techincal Journal 29, 147–160 (1950)MathSciNetGoogle Scholar
  10. 10.
    Montes-y-Gómez, M., Gelbukh, A., López-López, A.: Comparison of Conceptual Graphs. In: Cairó, O., Cantú, F.J. (eds.) MICAI 2000. LNCS, vol. 1793, pp. 548–556. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  11. 11.
    Rasmussen, E.: Clustering Algorithms. In: Frakes, W., Baeza-Yates, R. (eds.) Information Retrieval: Data structures & Algorithms, Prentice-Hall, Englewood Cliffs (1992)Google Scholar
  12. 12.
    Golbeck, J., Parsia, B., Hendler, J.: Trust Networks on the Semantic Web. In: Proceedings of Cooperative Intelligent Agents 2003, Helsinki, Finland (2003)Google Scholar
  13. 13.
    Zaki, M.J., Aggarwal, C.C.: XRules: An Effective Structural Classifier for XML Data. In: 9th International Conference on Knowledge Discovery and Data-mining, pp. 316–325 (2003)Google Scholar
  14. 14.
    Berendt, B., Hotho, A., Stumme, G.: Towards Semantic Web Mining. In: International Semantic Web Conference, pp. 264–278 (2002)Google Scholar
  15. 15.
    Alani, H., Dasmahapatra, S., O’Hara, K., Shadbolt, N.: Identifying Communities of Practice through Ontology Network Analysis. In: IEEE IS, pp. 18–25. IEEE, Los Alamitos (2003)Google Scholar
  16. 16.
    Middleton, S., Alani, H., Shadbolt, N., De Roure, D.: Exploiting Synergy Between Ontologies and Recommender Systems. In: 11th International WWW Conference, Semantic Web Workshop, pp. 41–50 (2002)Google Scholar
  17. 17.
    Middleton, S., Shadbolt, N., Roure, D.D.: Ontological User Profiling in Recommender Systems. ACM Transactions on Information Systems 22(1), 54–88 (2004)CrossRefGoogle Scholar
  18. 18.
    Dolog, P., Henze, N., Nejdl, W., Sintek, M.: Towards the Adaptive Semantic Web. In: 1st Workshop on Principles and Practice of Semantic Web Reasoning, pp. 51–68 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Gunnar AAstrand Grimnes
    • 1
  • Pete Edwards
    • 1
  • Alun Preece
    • 1
  1. 1.Computing Science DeptKing’s College, University of Aberdeen

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