Abstract
Time modeling is a crucial feature in many application domains. However, temporal information often is not crisp, but is uncertain, subjective and vague. This is particularly true when representing historical information, as historical accounts are inherently imprecise. Similarly, we conjecture that in the Semantic Web representing uncertain temporal information will be a common requirement. Hence, existing approaches for temporal modeling based on crisp representation of time cannot be applied to these advanced modeling tasks. To overcome these difficulties, in this paper we present fuzzy interval-based temporal model capable of representing imprecise temporal knowledge. Our approach naturally subsumes existing crisp temporal models, i.e. crisp temporal relationships are intuitively represented in our system. Apart from presenting the fuzzy temporal model, we discuss how this model is integrated with the ontology model to allow annotating ontology definitions with time specifications.
This work was partially supported by the EU in the framework of the VICODI project (EU-IST-2001-37534)
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Jensen, C.S., Dyreson, C.E., Böhlen, M., Clifford, J., Elmasri, R., Gadia, S.K., Grandi, F., Hayes, P., Jajodia, S., Käfer, W., Kline, N., Lorentzos, N., Mitsopoulos, Y., Montanari, A., Nonen, D., Peressi, E., Pernici, B., Roddick, J.F., Sarda, N.L., Scalas, M.R., Segev, A., Snodgrass, R.T., Soo, M.D., Tansel, A., Tiberio, P., Wiederhold, G.: The consensus glossary of temporal database concepts – february 1998 version. In: Etzion, O., Jajodia, S., Sripada, S. (eds.) Dagstuhl Seminar 1997. LNCS, vol. 1399, pp. 367–405. Springer, Heidelberg (1998)
Etzion, O., Jajodia, S., Sripada, S. (eds.): Temporal databases: research and practice; Etzion, O., Jajodia, S., Sripada, S. (eds.): Temporal databases: research and practice, New York, NY, USA. LNCS, vol. 1399. Springer, Heidelberg (1998)
Vila, L.: A survey on temporal reasoning in artificial intelligence. AICOM (Artificial Intelligence Communications) 7, 4–28 (1994)
Allen, J.F.: Maintaining knowledge about temporal intervals. Communications of the ACM 26, 832–843 (1983)
Dubois, D., Prade, H.: Processing fuzzy temporal knowledge. IEEE Transactions of Systems, Man and Cybernetics 19, 729–744 (1989)
Dutta, S.: An event-based fuzzy temporal logic. In: Proc. 18th IEEE Intl. Symp. on Multiple-Valued Logic, Palma de Mallorca, Spain, pp. 64–71 (1988)
Godo, L., Vila, L.: Possibilistic temporal reasoning based on fuzzy temporal constraints. In: Mellish, C. (ed.) IJCAI 1995: Proceedings International Joint Conference on Artificial Intelligence, Montreal (1995)
DARPA Agent Markup Language project: DAML-Time Homepage (2002), Accessible from the URL http://www.cs.rochester.edu/~ferguson/daml/
Chomicki, J.: Temporal query languages: A survey. In: Gabbay, D.M., Ohlbach, H.J. (eds.) ICTL 1994. LNCS (LNAI), vol. 827, pp. 506–534. Springer, Heidelberg (1994)
McKenzie, E., Snodgrass, R.: An evaluation of relational algebras incorporating the time dimension in databases. ACM Computing Surveys 23, 501–543 (1991)
Zadeh, L.A.: Fuzzy sets. Information and Control 8, 338–353 (1965)
Motik, B., Maedche, A., Volz, R.: A conceptual modeling approach for semanticsdriven enterprise applications. In: Meersman, R., Tari, Z., et al. (eds.) CoopIS 2002, DOA 2002, and ODBASE 2002. LNCS, vol. 2519. Springer, Heidelberg (2002)
Brickley, D., Guha, R.: RDF Vocabulary Description Language 1.0: RDF Schema. W3C (2000), Accessible from the URL http://www.w3.org/TR/rdf-schema
Patel-Schneider, P.F., Hayes, P., Horrocks, I., van Harmelen, F.: Web Ontology Language (OWL) Abstract Syntax and Semantics. W3C (2002), Accessible from the URL http://www.w3.org/TR/owl-semantics/
Grossof, B., Horrocks, I., Volz, R., Decker, S.: Description logic programs: Combining logic programs with description logic. In: Proceedings of WWW 2003, Budapest, Hungary (2003)
Chen, W., Kifer, M., Warren, D.S.: Hilog: A foundation for higher-order logic programming. Journal of Logic Programming 15, 183–230 (1993)
Dekhtyar, A., Ross, R., Subrahmanian, V.S.: Probabilistic temporal databases, I: algebra. ACM Transactions on Database Systems (TODS) 26, 41–95 (2001)
Dyreson, C.E., Snodgrass, R.T.: Supporting valid-time indeterminacy. ACM Transactions on Database Systems 23, 1–57 (1998)
Dubois, D., Prade, H.: Possibility Theory: An Approach to Computerized Processing of Uncertainty. Plenum Press, New York (1986)
Zadeh, L.: Fuzzy sets as a basis for possibility. Fuzzy Sets and Systems 1, 3–28 (1978)
Kurutach, W.: Modelling fuzzy interval-based temporal information: a temporal database perspective. In: Proceedings of, IEEE International Conference on Fuzzy Systems, Yokohama, Japan, 741–748 (1995)
Dubois, D., Prade, H.: Fundamentals of Fuzzy Sets. FSHS 7. The handbooks of fuzzy sets series, vol. 7. Kluwer Academic Publishers, Dordrecht (2000)
Nguyen, H.T., Walker, E.A.: A First Course in Fuzzy Logic. CRC Press, New York (1997)
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Nagypál, G., Motik, B. (2003). A Fuzzy Model for Representing Uncertain, Subjective, and Vague Temporal Knowledge in Ontologies. In: Meersman, R., Tari, Z., Schmidt, D.C. (eds) On The Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE. OTM 2003. Lecture Notes in Computer Science, vol 2888. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39964-3_57
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DOI: https://doi.org/10.1007/978-3-540-39964-3_57
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