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A web system for reasoning with probabilistic OWL
Author(s) -
Bellodi Elena,
Lamma Evelina,
Riguzzi Fabrizio,
Zese Riccardo,
Cota Giuseppe
Publication year - 2017
Publication title -
software: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.437
H-Index - 70
eISSN - 1097-024X
pISSN - 0038-0644
DOI - 10.1002/spe.2410
Subject(s) - computer science , probabilistic logic , semantic reasoner , prolog , probabilistic argumentation , probabilistic ctl , semantics (computer science) , programming language , datalog , semantic web , theoretical computer science , description logic , knowledge base , world wide web , artificial intelligence , probabilistic analysis of algorithms
We present the web application 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.\udVarious probabilistic extensions of DLs have been proposed in the recent past, since uncertainty is a fundamental component of the Semantic Web.\udWe consider probabilistic DL theories following our DISPONTE semantics. Axioms of a DISPONTE Knowledge Base (KB) can be annotated with a probability and the probability of queries can be computed with inference algorithms.\udTRILL is a probabilistic reasoner for DISPONTE KBs that is implemented in Prolog and exploits its backtracking facilities for handling the non-determinism of the tableau algorithm.\udTRILL 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.\udIt is free, open, and accessible on the Web at the url: \trillurl; it includes a number of examples that cover a wide range of domains and provide interesting Probabilistic Semantic Web applications.\udBy 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