A web system for reasoning with probabilistic OWL

E Bellodi, E Lamma, F Riguzzi… - Software: Practice and …, 2017 - Wiley Online Library
Software: Practice and Experience, 2017Wiley Online Library
We present the web application Tableau Reasoner for descrIption Logics in proLog on SWI‐
Prolog for SHaring (TRILL on SWISH) which allows the user to write probabilistic description
logic (DL) theories and compute the probability of queries with just a web browser. Various
probabilistic extensions of DLs have been proposed in the recent past, because uncertainty
is a fundamental component of the Semantic Web. We consider probabilistic DL theories
following our distribution semantics for probabilistic ontologies (DISPONTE) semantics …
Summary
We present the web application Tableau Reasoner for descrIption Logics in proLog on SWI‐Prolog for SHaring (TRILL on SWISH) which allows the user to write probabilistic description logic (DL) theories and compute the probability of queries with just a web browser. Various probabilistic extensions of DLs have been proposed in the recent past, because uncertainty is a fundamental component of the Semantic Web. We consider probabilistic DL theories following our distribution semantics for probabilistic ontologies (DISPONTE) semantics. Axioms of a DISPONTE knowledge base can be annotated with a probability, and the probability of queries can be computed with inference algorithms. TRILL is a probabilistic reasoner for DISPONTE knowledge base that is implemented in Prolog and exploits its backtracking facilities for handling the non‐determinism of the tableau algorithm. TRILL on SWISH is based on SWISH, a recently proposed web framework for logic programming, based on various features and packages of SWI‐Prolog (e.g., a web server and a library for creating remote Prolog engines and posing queries to them). TRILL on SWISH also allows users to cooperate in writing a probabilistic DL theory. It is free, open, and accessible on the Web at the url: http://trill.lamping.unife.it; it includes a number of examples that cover a wide range of domains and provide interesting Probabilistic Semantic Web applications. By building a web‐based system, we allow users to experiment with probabilistic DLs without the need to install a complex software stack. In this way, we aim to reach out to a wider audience and popularize the Probabilistic Semantic Web. Copyright © 2016 John Wiley & Sons, Ltd.
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