Semantic Web Reasoning Using a Blackboard System

  • Craig McKenzie
  • Alun Preece
  • Peter Gray
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4187)


In this paper, we discuss the need for a hybrid reasoning approach to handing Semantic Web (SW) data and explain why we believe that the Blackboard Architecture is particularly suitable. We describe how we have utilised it for coordinating a combination of ontological inference, rules and constraint based reasoning within a SW context.

After describing the metaphor on which the Blackboard Architecture is based we introduce its key components: the blackboard Panels containing the solution space facts and problem related goals and sub-goals; the differing behaviours of the associated Knowledge Sources and how they interact with the blackboard; and, finally, the Controller and how it manages and focuses the problem solving effort.

To help clarify, we use our test-bed system, the AKTive Workgroup Builder and Blackboard (AWB+B) to explain some of the issues and problems encountered when implementing a SW Blackboard System in a problem oriented context.


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  1. 1.
    Anastasia Analyti, C.V.D., Antoniou, G., Wagner, G.: Negation and Negative Information in the W3C Resource Description Framework. Annals of Mathematics, Computing & Teleinfomatics 1(2), 25–34 (2004)Google Scholar
  2. 2.
    Brachman, R., Gilbert, V., Levesque, H.: An Essential Hybrid Reasoning System: Knowledge and Symbol Level Accounts of KRYPTON. In: The Ninth International Joint Conference on Artificial Intelligence (IJCAI 1985), Los Angeles, California, USA, pp. 532–539 (1985)Google Scholar
  3. 3.
    Carver, N., Lesser, V.: The Evolution of Blackboard Control Architectures. CMPSCI Technical Report 92-71, Computer Science Department, Southern Illinois University (1992)Google Scholar
  4. 4.
    Doherty, P., Lukaszewicz, W., Szalas, A.: Efficient Reasoning using the Local Closed-World Assumption. In: 9th International Conference on Artificial Intelligence: Mehtodology, Systems, Applications (2000)Google Scholar
  5. 5.
    Engelmore, R.S., Terry, A.: Structure and Function of the CRYSALIS System. In: The Sixth International Joint Conference on Artificial Intelligence (IJCAI 1979), Tokyo, Japan, August 20-23, pp. 250–256 (1979)Google Scholar
  6. 6.
    Erman, L.D., Hayes-Roth, F., Lesser, V.R., Reddy, D.R.: The Hearsay-II Speech-Understanding System: Integrating Knowledge to Resolve Uncertainty. ACM Computing Surveys 12(2), 213–253 (1980)CrossRefGoogle Scholar
  7. 7.
    Etzioni, O., Golden, K., Weld, D.: Sound and Efficient Closed-World Reasoning for Planning. Artificial Intelligence 89(1-2), 113–148 (1997)MATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Feigenbaum, E.A., Nii, H.P., Anton, J.J., Rockmore, A.J.: Signal-to-signal Transformation: HASP/SIAP Case Study. AI Magazine 3(2), 23–35 (1982)Google Scholar
  9. 9.
    Harris, S., Gibbins, N.: 3store: Efficient Bulk RDF Storage. In: 1st International Workshop on Practical and Scalable Semantic Systems (PSSS 2003), pp. 1–20 (2003)Google Scholar
  10. 10.
    Hayes-Roth, B.: A Blackboard Architecture for Control. Artificial Intelligence 26(3), 251–321 (1985)CrossRefGoogle Scholar
  11. 11.
    Hayes-Roth, B., Hayes-Roth, F., Rosenschien, F., Cammarata, S.: Modelling Planning as an Incremental, Opportunistic Process. In: The Sixth International Joint Conference on Artificial Intelligence (IJCAI 1979), Tokyo, Japan, August 20-23, 1979, pp. 375–383 (1979)Google Scholar
  12. 12.
    Hendler, J.: Agents and the Semantic Web. IEEE Intelligent Systems 16(2), 30–37 (2001)CrossRefGoogle Scholar
  13. 13.
    McKenzie, C., Preece, A., Gray, P.: Extending SWRL to Express Fully-Quantified Constraints. In: Antoniou, G., Boley, H. (eds.) RuleML 2004. LNCS, vol. 3323, pp. 139–154. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  14. 14.
    Mei, J., Liu, S., Yue, A., Lin, Z.: An Extension to OWL with General Rules. In: Antoniou, G., Boley, H. (eds.) RuleML 2004. LNCS, vol. 3323, pp. 155–169. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  15. 15.
    Nii, H.P.: Blackboard Systems: The Blackboard Model of Problem Solving and the Evolution of Blackboard Architectures. AI Magazine 7(2), 38–53 (1986)Google Scholar
  16. 16.
    Shadbolt, N., Gibbins, N., Glaser, H., Harris, S., Schraefel, M.: CS AKTive Space, or How We Learned to Stop Worrying and Love the Semantic Web. IEEE Intelligent Systems 19(3), 41–47 (2004)CrossRefGoogle Scholar
  17. 17.
    Tsarkov, D., Horrocks, I.: DL Reasoner vs. First-Order Prover. In: 2003 Description Logic Workshop (DL 2003). CEUR, vol. 81, pp. 152–159 (2003),

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Craig McKenzie
    • 1
  • Alun Preece
    • 1
  • Peter Gray
    • 1
  1. 1.Department of Computing ScienceUniversity of AberdeenAberdeenUK

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