Abstract
The development of retrieval models has been a major concern of the Information Retrieval (IR) community for the last two decades. As a result of this effort, we now have a few well established and widely known models, around which IR systems have been built and put at work on real applications. These models are based on different views of the retrieval process, but they all share two common features: first, they have been developed for and mostly applied only to textual documents; second, they adopt an indirect approach, based on statistical properties of key words, to the central problem of IR: capturing document contents. Both these features were dictated by the context in which the relevant research took place. As for the former, text was the only medium that could be automatically processed in an efficient way until a few years ago. As for the latter, the choice of a “surface” approach to capturing meaning was imposed by three factors: (1) the sheer size of major applications, where collections of thousands or millions of textual objects were addressed, thus making automatic extraction of document representations a necessity; (2) the lack of tools for automatically extracting more faithful renditions of document semantics; (3) the lack of theories that give a satisfactory explanation of what document semantics really is.
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
Abiteboul, S., Hull, R., and Vianu, V. (1995). Foundations of databases. Addison-Wesley, New York, NY.
Anderson, A. R. and Belnap, N. D. (1975). Entailment — the logic of relevance and necessity, volume 1. Princeton University Press, Princeton, NJ.
Belkin, N. J. (1981). Ineffable concepts in information retrieval. In Sparck Jones, K., editor, Information retrieval experiment, pages 44–58. Butterworths, London, UK.
Belnap, N. D. (1977). How a computer should think. In Ryle, G., editor, Contemporary aspects of philosophy, pages 30–56. Oriel Press, Stocksfield, UK.
Borgida, A. (1995). Description logics in data management. IEEE Transactions on Data and Knowledge Engineering, 7(5):671–682.
Borgida, A. and Patel-Schneider, P. F. (1994). A semantics and complete algorithm for subsumption in the CLASSIC description logic. Journal of Artificial Intelligence Research, 1:277–308.
Buchheit, M., Donini, F. M., and Schaerf, A. (1993). Decidable reasoning in terminological knowledge representation systems. Journal of Artificial Intelligence Research, 1:109–138.
Buongarzoni, P., Meghini, C., Salis, R., Sebastiani, F., and Straccia, U. (1995). Logical and computational properties of the description logic MIRTL. In Borgida, A., Lenzerini, M., Nardi, D., and Nebel, B., editors, Proc. of DL-95, 4th International Workshop on Description Logics, pages 80–84, Roma, Italy.
Chen, J. and Kundu, S. (1996). A sound and complete fuzzy logic system using Zadeh’s implication operator. In Ras, Z. W. and Maciek, M., editors, Proc. of the 9th Int. Sym. on Methodologies for Intelligent Systems (ISMIS-96), number 1079 in Lecture Notes In Artificial Intelligence, pages 233–242. Springer-Verlag.
D’Agostino, M. and Mondadori, M. (1994). The taming of the cut. Classical refutations with analytical cut. Journal of Logic and Computation, 4(3):285–319.
Davis, M., Longemann, G., and Loveland, D. (1962). A machine program for theorem proving. Journal of the ACM, 5(7):394–397.
Donini, F. M, Lenzerini, M., Nardi, D., and Nutt, W. (1991). The complexity of concept languages. In Proc. of KR-91, 2nd International Conference on Principles of Knowledge Representation and Reasoning, pages 151–162, Cambridge, MA.
Donini, F. M., Lenzerini, M., Nardi, D., Nutt, W., and Schaerf, A. (1992). Adding epistemic operators to concept languages. In KR-92, pages 342–353. Morgan Kaufmann.
Donini, F. M., Lenzerini, M., Nardi, D., Nutt, W., and Schaerf, A. (1994). Queries, rules and definitions as epistemic sentences in concept languages. In Proc. of the ECAI-94 Workshop on Knowledge Representation and Reasoning, number 810 in Lecture Notes in Computer Science, pages 113–132. Springer.
Dubois, D. and Prade, H. (1980). Fuzzy Sets and Systems. Academic Press, New York, NJ.
Dubois, D. and Prade, H. (1986). Possibilistic logic. In Gabbay, D. M. and Hogger, C. J., editors, Handbook of Logic in Artificial Intelligence, volume 3, pages 439–513. Clarendon Press, Dordrecht, NL.
Dunn, J. M. (1976). Intuitive semantics for first-degree entailments and coupled trees. Philosophical Studies, 29:149–168.
Dunn, J. M. (1986). Relevance logic and entailment. In Gabbay, D. M. and Guenthner, F., editors, Handbook of Philosophical Logic, volume 3, pages 117–224. Reidel, Dordrecht, NL.
Fitting, M. (1990). First-Order Logic and Automated Theorem Proving. Springer-Verlag.
Gelfond, M. and Przymusinska, H. (1986). Negation as failure: careful closure procedure. Artificial Intelligence, 30:273–287.
Giunchiglia, F. and Sebastiani, R. (1996a). Buiding decision procedures for modal logics from propositional decision procedures — the case study of modal K. In Proc. of the 13th Conf. on Automated Deduction (CADE-96), number 449 in Lecture Notes In Artificial Intelligence. Springer-Verlag.
Giunchiglia, F. and Sebastiani, R. (1996b). A SAT-based decision procedure for ALC. In Proc. of the 6th Int. Conf. on the Principles of Knowledge Representation and Reasoning (KR-96).
Haack, S. (1978). Philosophy of logics. Cambridge University Press, Cambridge, UK.
Halpern, J. Y. (1990). An analysis of first-order logics of probability. Artificial Intelligence, 46:311–350.
Heinsohn, J. (1994). Probabilistic description logics. In de Mantara, R. L. and Pool, D., editors, Proc. of the 10th Conference on Uncertainty in Artificila Intelligence, pages 311–318.
Hobbs, J. R. and Rosenschein, S. J. (1978). Making computational sense of Montague’s intensional logic. Artificial Intelligence, 9:287–306.
Hollunder, B. (1994). An alternative proof method for possibilistic logic and its application to terminological logics. In 10th Annual Conference on Uncertainty in Artificial Intelligence, Seattle, WA.
Ishizuka, M. and Kanai, N. (1985). Prolog-ELF: incorporating fuzzy logic. In Proc. of the 9th Int. Joint Conf. on Artificial Intelligence (IJCA1-85), pages 701–703, Los Angeles, CA.
Jäger, M. (1994). Probabilistic reasoning in terminological logics. In Proc. of KR-94, 5-th International Conference on Principles of Knowledge Representation and Reasoning, pages 305–316, Bonn, FRG.
Lee, R. C. T. (1972). Fuzzy logic and the resolution principle. Journal of the ACM, 19(l):109–119.
Levesque, H. J. (1984). A logic of implicit and explicit belief. In Proc. of AAAI-84, 4th Conference of the American Association for Artificial Intelligence, pages 198–202, Austin, TX.
Levesque, H. J. (1988). Logic and the complexity of reasoning. Journal of Philosophical Logic, 17:355–389.
Łlukaszewicz, W. (1990). Nonmonotonic reasoning: formalization of commonsense reasoning. Ellis Horwood, Chichester, UK.
Meghini, C., Sebastiani, F., and Straccia, U. (1997). The terminological image retrieval model. In Proc. of ICIAP′97, International Conference On Image Analysis And Processing, volume II, pages 156–163, Florence, I.
Meghini, C., Sebastiani, F., Straccia, U., and Thanos, C. (1993). A model of information retrieval based on a terminological logic. In Proc. of SIGIR-93, 16th ACM International Conference on Research and Development in Information Retrieval, pages 298–307, Pittsburgh, PA.
Meghini, C. and Straccia, U. (1996a). Information retrieval: Foundations of a description logic-based approach. Technical Report B4-18-06-96, Istituto di Elaborazione della Informazione, Consiglio Nazionale delle Ricerche, Pisa, Italy.
Meghini, C. and Straccia, U. (1996b). A relevance terminological logic for information retrieval. In Proc. of S1GIR-96, 19th International Conference on Research and Development in Information Retrieval, pages 197–205, Zurich, Switzerland.
Nelson, E. (1933). On three logical principles in intension. The Monist, 43.
Patel-Schneider, P. F. (1986). A four-valued semantics for frame-based description languages. In Proc. of AAAI-86, 5th Conference of the American Association for Artificial Intelligence, pages 344–348, Philadelphia, PA.
Patel-Schneider, P. F. (1987). A hybrid, decidable, logic-based knowledge representation system. Computational Intelligence, 3:64–77.
Patel-Schneider, P. F. (1989). A four-valued semantics for terminological logics. Artificial Intelligence, 38:319–351.
Reiter, R. (1978). On closed-world data bases. In Gallaire, H. and Minker, J., editors, Logic and Data Bases, pages 55–76. Plenum Press.
Reiter, R. (1990). On asking what a database knows. In Lloyd, J., editor, Proc. of the Symposium on Computational Logic, pages 96–113. Springer Verlag.
Saracevic, T. (1975). Relevance: a review of and a framework for thinking on the notion of information science. Journal of the American Society for Information Science, 26:321–343.
Schmidt-Schauss, M. and Smolka, G. (1991). Attributive concept descriptions with complements. Artificial Intelligence, 48:1–26.
Sebastiani, F. (1994). A probabilistic terminological logic for modelling information retrieval. In Proc. of SIGIR-94, 17th ACM International Conference on Research and Development in Information Retrieval, pages 122–130, Dublin, IRL. Published by Springer Verlag, Heidelberg, FRG.
Straccia, U. (1996). Document retrieval by relevance terminological logics. In Ruthven, I., editor, Proc. of MIRO-95, Workshop on Multimedia Information Retrieval, Glasgow, UK. Springer Verlag, Heidelberg, FRG.
Straccia, U. (1997). A sequent calculus for reasoning in four-valued description logics. In Proc. of the Int. Conf. on Analytic Tableaux and Related Methods (TABLEAUX-97), Pont-à-Mousson, France.
van Rijsbergen, C. (1979). Information Retrieval. Butterworths, London, GB, second edition.
van Rijsbergen, C. J. (1989). Towards an information logic. In Proc. of SIGIR-89, 12th ACM International Conference on Research and Development in Information Retrieval, pages 77–86, Cambridge, MA.
Wagner, G. (1991). Ex contradictione nihil sequitur. In Proc. of IJCAI-91, 12th Intemational Joint Conference on Artificial Intelligence, pages 538–543, Sidney, Australia.
Yager, R. R. (1995). Fuzzy sets as a tool for modeling. In van Leeuwen, J., editor, Computer Science Today, number 1000 in Lecture Notes in Computer Science, pages 536–548. Springer-Verlag.
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3):338–353.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1998 Springer Science+Business Media New York
About this chapter
Cite this chapter
Meghini, C., Sebastiani, F., Straccia, U. (1998). Mirlog: A Logic for Multimedia Information Retrieval. In: Crestani, F., Lalmas, M., van Rijsbergen, C.J. (eds) Information Retrieval: Uncertainty and Logics. The Kluwer International Series on Information Retrieval, vol 4. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-5617-6_7
Download citation
DOI: https://doi.org/10.1007/978-1-4615-5617-6_7
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4613-7570-8
Online ISBN: 978-1-4615-5617-6
eBook Packages: Springer Book Archive