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

Riccardo Zese,Fabrizio Riguzzi,Evelina Lamma,Giuseppe Cota,Elena Bellodi

doi:10.1017/s1471068418000480

Abstract

AbstractWhen 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 thetableau algorithm, which has to handle non-determinism. Prolog, with its efficient handling of non-determinism, is suitable for implementing the tableau algorithm. TRILL and TRILLPare systems offering a Prolog implementation of the tableau algorithm. TRILLPbuilds apinpointing formulathat 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 athttp://trill.ml.unife.it/.

Highlights

The objective of the Semantic Web is to make information available in a form that is understandable and automatically manageable by machines
We present TORNADO for “Trill powered by pinpOinting foRmulas and Probabilistic Description Logics (DLs) Reasoning with Pinpointing Formulas: A Prolog-based Approach 3
For Konclude, in order to perform tests as close as possible with the other competitors, for each subclass-of test with query C D we extended the knowledge bases (KBs) with one test concept defined as ¬(C ∧ ¬D), while for each instance-of query a : C, where a belongs to the concepts C1, ..., Cn, we extended the KB with one test concept defined as ¬(CC ∧ ¬D), where CC is defined as the intersection of C1, ..., Cn

Summary

Introduction

The objective of the Semantic Web is to make information available in a form that is understandable and automatically manageable by machines. In order to realize this vision, the W3C supported the development of a family of knowledge representation formalisms of increasing complexity for defining ontologies, called Web Ontology Languages (OWL), based on Description Logics (DLs). In order to fully support the development of the Semantic Web, efficient DL reasoners are essential. The most common approach adopted by reasoners is the tableau algorithm (Horrocks and Sattler 2007), written in a procedural language. This algorithm applies some expansion rules on a tableau, a representation of the assertional part of the KB. Pellet (Sirin et al 2007), for instance, is a reasoner written in Java

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Conclusion