The need for an automatic inference process able to deal with information coming from unreliable sources is becoming a relevant issue both on corporate networks and on the open Web. Mathematical theories to reason with uncertain information have been successfully applied in several situations, but each one of these models is tailored to deal with a specific semantics of uncertainty. In this paper, we put forward the idea of using explicit representations of the different types of uncertainty for partitioning the inference process into parts. By coordinating multiple independent reasoning processes, we are sometimes able to apply a specific model to each type of uncertain information, and recombine the final results via a suitable reconciliation process. We validated our approach applying it to the classic schema matching problem, and using the Ontology Alignment Evaluation Initiative, (OAEI) tests to assess the results.

A toward framework for generic uncertainty management / E. Damiani, P. Ceravolo, M. Leida - In: 2009 International fuzzy systems association world congress, 2009 European society for fuzzy logic and technology conference : proceedings : Lisbon, Portugal, 20-24 july, 2009 / [a cura di] J. P. Carvalho [et al.]. - [s.l] : null, 2009. - ISBN 9789899507968. - pp. 1169-1176 (( convegno IFSA-EUSFLAT tenutosi a Lisbona nel 2009.

A toward framework for generic uncertainty management

E. Damiani
Primo
;
P. Ceravolo
Secondo
;
M. Leida
Ultimo
2009

Abstract

The need for an automatic inference process able to deal with information coming from unreliable sources is becoming a relevant issue both on corporate networks and on the open Web. Mathematical theories to reason with uncertain information have been successfully applied in several situations, but each one of these models is tailored to deal with a specific semantics of uncertainty. In this paper, we put forward the idea of using explicit representations of the different types of uncertainty for partitioning the inference process into parts. By coordinating multiple independent reasoning processes, we are sometimes able to apply a specific model to each type of uncertain information, and recombine the final results via a suitable reconciliation process. We validated our approach applying it to the classic schema matching problem, and using the Ontology Alignment Evaluation Initiative, (OAEI) tests to assess the results.
Ontology matching; Reasoning; Rules; Uncertainty
Settore INF/01 - Informatica
IFSA (International Fuzzy systems Association)
EUSFLAT (European Society for Fuzzy Logic and Technology)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/71098
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