Skip to main content
SpringerLink
Log in
Book cover

Soft Computing in Ontologies and Semantic Web pp 79–101Cite as

The SP Theory and the Representation and Processing of Knowledge

  • Chapter

  • 463 Accesses

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 204))

Abstract

This chapter describes an approach to the representation and processing of knowledge, based on the SP theory of computing and cognition. This approach has strengths that complement others such as those currently proposed for the Semantic Web. The benefits of the SP approach are simplicity and comprehensibility in the representation of knowledge, an ability to cope with errors and uncertainties in knowledge, and capabilities for ‘intelligent’ processing of knowledge, including probabilistic reasoning, pattern recognition, information retrieval, unsupervised learning, planning and problem solving.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. F. Baader, R. Küsters, A. Borgida, and D. L. McGuinness. Matching in description logics. Journal of Logic and Computation, 9(3):411–447, 1999.

    Article  MATH  Google Scholar 

  2. T. Berners-Lee, J. Hendler, and O. Lassila. The Semantic Web. Scientific American, May 2001.

    Google Scholar 

  3. E. Bertino, B. Catania, and G. P. Zarri. Intelligent Database Systems. Addison-Wesley, Harlow, 2001.

    Google Scholar 

  4. S. Brandt and A.-Y. Turhan. Using non-standard inferences in description logics: what does it buy me? In Proceedings of KI-2001 Workshop on Applications of Description Logic (KIDLWS'01), 2001.

    Google Scholar 

  5. J. Broekstra, M. Klein, S. Decker, D. Fensel, F. van Harmelin, and I. Horrocks. Enabling knowledge representation on the web by extending RDF schema. Computer Networks, 39:609–634, 2002.

    Article  Google Scholar 

  6. D. Fensel, I. Horrocks, F. van Harmelin, D. L. McGuinness, and P. F. Patel-Schneider. OIL: an ontology infrastructure for the Semantic Web. IEEE Intelligent Systems, 16(2):38–45, 2001.

    Article  Google Scholar 

  7. M. L. Ginsberg. AI and nonmonotonic reasoning. In D. M. Gabbay, C. J. Hogger, and J. A. Robinson, editors, Handbook of Logic in Artificial Intelligence and Logic Programming: Nonmonotonic Reasoning and Uncertain Reasoning, volume 3, pages 1–33. Oxford University Press, Oxford, 1994.

    Google Scholar 

  8. M. Gold. Language identification in the limit. Information and Control, 10:447–474, 1967.

    Article  Google Scholar 

  9. P. Hayes. Catching the dreams. Technical report, 2002. Copy: www.aifb.unikarlsruhe. de/ sst/is/WebOntologyLanguage/hayes.htm .

    Google Scholar 

  10. I. Horrocks. Reasoning with expressive description logics: theory and practice. In A. Voronkov, editor, Proceedings of the 18th International Conference on Automated Deduction (CADE-18), volume 2392 of Lecture Notes in Artificial Intelligence, pages 1–15. Springer-Verlag, 2002.

    Google Scholar 

  11. M. Li and P. Vitányi. An Introduction to Kolmogorov Complexity and Its Applications. Springer-Verlag, New York, 1997.

    MATH  Google Scholar 

  12. D. L. McGuinness, R. Fikes, J. Hendler, and L. A. Stein. DAML+OIL: an ontology language for the Semantic Web. IEEE Intelligent Systems, 17(5):72–80, 2002.

    Article  Google Scholar 

  13. J. Pearl. Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, San Francisco, revised second printing edition, 1997.

    Google Scholar 

  14. J. Rissanen. Modelling by the shortest data description. Automatica-J, IFAC, 14:465–471, 1978.

    Article  MATH  Google Scholar 

  15. D. Sankoff and J. B. Kruskall. TimeWarps, String Edits, and Macromolecules: the Theory and Practice of Sequence Comparisons. Addison-Wesley, Reading, MA, 1983.

    Google Scholar 

  16. R. R. Sokal and P. H. A. Sneath, editors. Numerical Taxonomy: the Principles and Practice of Numerical Classification. W. H. Freeman, San Francisco, 1973.

    MATH  Google Scholar 

  17. R. J. Solomonoff. A formal theory of inductive inference. parts I and II. Information and Control, 7:1–22 and 224–254, 1964.

    Article  Google Scholar 

  18. C. S. Wallace and D. M. Boulton. An information measure for classification. Computer Journal, 11(2):185–195, 1968.

    MATH  Google Scholar 

  19. J. G. Wolff. Learning syntax and meanings through optimization and distributional analysis. In Y. Levy, I. M. Schlesinger, and M. D. S. Braine, editors, Categories and Processes in Language Acquisition, pages 179–215. Lawrence Erlbaum, Hillsdale, NJ, 1988. Copy: www.cognitionresearch.org.uk/lang learn.html#wolff 1988.

    Google Scholar 

  20. J. G. Wolff. A scaleable technique for best-match retrieval of sequential information using metrics-guided search. Journal of Information Science, 20(1):16–28, 1994. Copy: www.cognitionresearch.org.uk/papers/ir/ir.htm .

    Article  Google Scholar 

  21. J. G. Wolff. ‘Computing’ as information compression by multiple alignment, unification and search. Journal of Universal Computer Science, 5(11):777–815, 1999. Copy: http://arxiv.org/abs/cs.AI/0307013 .

    MATH  Google Scholar 

  22. J. G. Wolff. Probabilistic reasoning as information compression by multiple alignment, unification and search: an introduction and overview. Journal of Universal Computer Science, 5(7):418–462, 1999. Copy: http://arxiv.org/abs/cs.AI/0307010 .

    Google Scholar 

  23. J. G. Wolff. Syntax, parsing and production of natural language in a framework of information compression by multiple alignment, unification and search. Journal of Universal Computer Science, 6(8):781–829, 2000. Copy: http://arxiv.org/abs/cs.AI/0307014 .

    MATH  Google Scholar 

  24. J. G. Wolff. Information compression by multiple alignment, unification and search as a framework for human-like reasoning. Logic Journal of the IGPL, 9(1):205–222, 2001. First published in the Proceedings of the International Conference on Formal and Applied Practical Reasoning (FAPR 2000), September 2000, ISSN 1469–4166. Copy: www.cognitionresearch.org.uk/papers/pr/pr.htm .

    Google Scholar 

  25. J. G. Wolff. Mathematics and logic as information compression by multiple alignment, unification and search. Technical report, CognitionResearch.org.uk, 2002. Copy: http://arxiv.org/abs/math.GM/0308153 .

    Google Scholar 

  26. J. G. Wolff. Unsupervised learning in a framework of information compression by multiple alignment, unification and search. Technical report, CognitionResearch.org.uk, 2002. Copy: http://arxiv.org/abs/cs.AI/0302015 .

    Google Scholar 

  27. J. G. Wolff. Information compression by multiple alignment, unification and search as a unifying principle in computing and cognition. Artificial Intelligence Review, 19(3):193–230, 2003. Copy: http://arxiv.org/abs/cs.AI/0307025 .

    Article  Google Scholar 

  28. J. G. Wolff. Unsupervised grammar induction in a framework of information compression by multiple alignment, unification and search. In C. de la Higuera, P. Adriaans, M. van Zaanen, and J. Oncina, editors, Proceedings of the Workshop and Tutorial on Learning Context-Free Grammars, pages 113–124, 2003. This workshop was held in association with the 14th European Conference on Machine Learning and the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2003), September 2003, Cavtat-Dubrovnik, Croata. Copy: http://arxiv.org/abs/cs.AI/0311045 .

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. CognitionResearch.org.uk, Menai Bridge, UK

    J. Gerard Wolff

Authors
  1. J. Gerard Wolff
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. College of Information Science and Engineering, Northeastern University Shenyang, Liaoning, 110004, China

    Zongmin Ma Dr.

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer

About this chapter

Cite this chapter

Wolff, J.G. (2006). The SP Theory and the Representation and Processing of Knowledge. In: Ma, Z. (eds) Soft Computing in Ontologies and Semantic Web. Studies in Fuzziness and Soft Computing, vol 204. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-33473-6_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-33473-6_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33472-9

  • Online ISBN: 978-3-540-33473-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation