Computing Probabilistic Least Common Subsumers in Description Logics

Authors:
Thomas Mantay;Ralf Möller;Alissa Kaplunova
Affiliations:
-;-;-
Venue:
KI '99 Proceedings of the 23rd Annual German Conference on Artificial Intelligence: Advances in Artificial Intelligence
Year:
1999

Citing 3
Cited 1

A Polynomial Approach to the Constructive Induction of Structural Knowledge

Machine Learning - Special issue on evaluating and changing representation
Computing the Least Common Subsumer and the Most Specific Concept in the Presence of Cyclic ALN-Concept Descriptions

KI '98 Proceedings of the 22nd Annual German Conference on Artificial Intelligence: Advances in Artificial Intelligence
P-CLASSIC: a tractable probablistic description logic

AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence

Managing Uncertainty and Vagueness in Description Logics, Logic Programs and Description Logic Programs

Reasoning Web

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Abstract

Computing least common subsumers in description logics is an important reasoning service useful for a number of applications. As shown in the literature, it can, for instance, be used for similarity-based information retrieval where information retrieval is performed on the basis of the similarities of user-specified examples. In this article, we first show that, for crisp DLs, in certain cases the set of retrieved information items can be too large. Then we propose a probabilistic least common subsumer operation based on a probabilistic extension of the description logic language ALN. We show that by this operator the amount of retrieved data can be reduced avoiding information flood.