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Probabilistic DL Reasoning with Pinpointing Formulas: A Prolog-based Approach

Published online by Cambridge University Press:  14 January 2019

RICCARDO ZESE*
Affiliation:
Dipartimento di Ingegneria – Università di Ferrara, Via Saragat 1, 44122, Ferrara, Italy (e-mails: [email protected], [email protected], [email protected])
GIUSEPPE COTA
Affiliation:
Dipartimento di Ingegneria – Università di Ferrara, Via Saragat 1, 44122, Ferrara, Italy (e-mails: [email protected], [email protected], [email protected])
EVELINA LAMMA
Affiliation:
Dipartimento di Ingegneria – Università di Ferrara, Via Saragat 1, 44122, Ferrara, Italy (e-mails: [email protected], [email protected], [email protected])
ELENA BELLODI
Affiliation:
Dipartimento di Matematica e Informatica – Università di Ferrara, Via Saragat 1, 44122, Ferrara, Italy (e-mails: [email protected], [email protected])
FABRIZIO RIGUZZI
Affiliation:
Dipartimento di Matematica e Informatica – Università di Ferrara, Via Saragat 1, 44122, Ferrara, Italy (e-mails: [email protected], [email protected])

Abstract

When modeling real-world domains, we have to deal with information that is incomplete or that comes from sources with different trust levels. This motivates the need for managing uncertainty in the Semantic Web. To this purpose, we introduced a probabilistic semantics, named DISPONTE, in order to combine description logics (DLs) with probability theory. The probability of a query can be then computed from the set of its explanations by building a Binary Decision Diagram (BDD). The set of explanations can be found using the tableau algorithm, which has to handle non-determinism. Prolog, with its efficient handling of non-determinism, is suitable for implementing the tableau algorithm. TRILL and TRILLP are systems offering a Prolog implementation of the tableau algorithm. TRILLP builds a pinpointing formula that compactly represents the set of explanations and can be directly translated into a BDD. Both reasoners were shown to outperform state-of-the-art DL reasoners. In this paper, we present an improvement of TRILLP, named TORNADO, in which the BDD is directly built during the construction of the tableau, further speeding up the overall inference process. An experimental comparison shows the effectiveness of TORNADO. All systems can be tried online in the TRILL on SWISH web application at http://trill.ml.unife.it/.

Type
Original Article
Copyright
Copyright © Cambridge University Press 2019 

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Footnotes

This work was supported by Gruppo Nazionale per il Calcolo Scientifico (GNCS-INdAM).

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