Abstract
Approximate reasoning for the Semantic Web is based on the idea of sacrificing soundness or completeness for a significant speed-up of reasoning. This is to be done in such a way that the number of introduced mistakes is at least outweighed by the obtained speed-up. When pursuing such approximate reasoning approaches, however, it is important to be critical not only about appropriate application domains, but also about the quality of the resulting approximate reasoning procedures. With different approximate reasoning algorithms discussed and developed in the literature, it needs to be clarified how these approaches can be compared, i.e. what it means that one approximate reasoning approach is better than some other. In this paper, we will formally define such a foundation for approximate reasoning research. We will clarify – by means of notions from statistics – how different approximate algorithms can be compared, and ground the most fundamental notions in the field formally. We will also exemplify what a corresponding statistical comparison of algorithms would look like.
Research reported in this paper was partially supported by the EU in the IST project NeOn (IST-2006-027595, http://www.neon-project.org/ ), by the Deutsche Forschungsgemeinschaft (DFG) under the ReaSem project, and by the German Federal Ministry of Education and Research (BMBF) under the Theseus project, http://theseus-programm.de .
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Fensel, D., Harmelen, F.V.: Unifying reasoning and search to web scale. IEEE Internet Computing 11, 94–96 (2007)
Dowling, W.P., Gallier, J.H.: Linear-time algorithms for testing the satisfiability of propositional Horn formulae. Journal of Logic Programming 1, 267–284 (1984)
Horvitz, E.J.: Reasoning about beliefs and actions under computational resource constraints. In: Kanal, L.N., Levitt, T.S., Lemmer, J.F. (eds.) Uncertainty in Artificial Intelligence 3, pp. 301–324. Elsevier, Amsterdam (1987)
Selman, B., Kautz, H.A.: Knowledge compilation using Horn approximations. In: Proceedings of the Ninth National Conference on Artificial Intelligence (AAAI 1991), pp. 904–909 (1991)
Schaerf, M., Cadoli, M.: Tractable reasoning via approximation. Artificial Intelligence 74, 249–310 (1995)
Cadoli, M., Schaerf, M.: Approximate inference in default reasoning and circumscription. Fundamenta Informaticae 23, 123–143 (1995)
Dalal, M.: Anytime clausal reasoning. Annals of Mathematics and Artificial Intelligence 22(3–4), 297–318 (1998)
Cadoli, M., Scarcello, F.: Semantical and computational aspects of Horn approximations. Artificial Intelligence 119 (2000)
van Harmelen, F., ten Teije, A.: Describing problem solving methods using anytime performance profiles. In: Proceedings of ECAI 2000, Berlin, August 2000, pp. 181–186 (2000)
Groot, P., ten Teije, A., van Harmelen, F.: Towards a structured analysis of approximate problem solving: a case study in classification. In: Proceedings of the Ninth International Conference on Principles of Knowledge Representation and Reasoning (KR 2004), Whistler, Colorado (2004)
Stuckenschmidt, H., van Harmelen, F.: Approximating terminological queries. In: Larsen, H., et al. (eds.) FQAS 2002. LNCS (LNAI), vol. 2522. Springer, Heidelberg (2002)
Horrocks, I., Li, L., Turi, D., Bechhofer, S.: The Instance Store: DL reasoning with large numbers of individuals. In: Proceedings of the International Workshop on Description Logics, DL 2004, Whistler, Canada, pp. 31–40 (2004)
Groot, P., Stuckenschmidt, H., Wache, H.: Approximating description logic classification for semantic web reasoning. In: Gómez-Pérez, A., Euzenat, J. (eds.) ESWC 2005. LNCS, vol. 3532, pp. 318–332. Springer, Heidelberg (2005)
Groot, P., Stuckenschmidt, H., Wache, H.: Approximating description logic classification for semantic web reasoning. In: Gómez-Pérez, A., Euzenat, J. (eds.) ESWC 2005. LNCS, vol. 3532. Springer, Heidelberg (2005)
Hitzler, P., Vrandecic, D.: Resolution-based approximate reasoning for OWL DL. In: Gil, Y., Motta, E., Benjamins, V.R., Musen, M.A. (eds.) ISWC 2005. LNCS, vol. 3729, pp. 383–397. Springer, Heidelberg (2005)
Pan, J.Z., Thomas, E.: Approximating OWL-DL ontologies. In: Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence, Vancouver, British Columbia, Canada, July 22-26, 2007, pp. 1434–1439. AAAI Press, Menlo Park (2007)
Wache, H., Groot, P., Stuckenschmidt, H.: Scalable instance retrieval for the semantic web by approximation. In: Dean, M., Guo, Y., Jun, W., Kaschek, R., Krishnaswamy, S., Pan, Z., Sheng, Q.Z. (eds.) WISE 2005 Workshops. LNCS, vol. 3807, pp. 245–254. Springer, Heidelberg (2005)
Stuckenschmidt, H.: Partial matchmaking using approximate subsumption. In: Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence, Vancouver, British Columbia, Canada, July 22-26, 2007, pp. 1459–1464. AAAI Press, Menlo Park (2007)
Tserendorj, T., Rudolph, S., Krötzsch, M., Hitzler, P.: Approximate OWL reasoning with Screech. In: Proceedings of the 2nd International Conference on Web Reasoning and Rule Systems, RR 2008, October 2008, Karlsruhe, Germany (to appear, 2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Rudolph, S., Tserendorj, T., Hitzler, P. (2008). What Is Approximate Reasoning?. In: Calvanese, D., Lausen, G. (eds) Web Reasoning and Rule Systems. RR 2008. Lecture Notes in Computer Science, vol 5341. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88737-9_12
Download citation
DOI: https://doi.org/10.1007/978-3-540-88737-9_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88736-2
Online ISBN: 978-3-540-88737-9
eBook Packages: Computer ScienceComputer Science (R0)