Representing uncertain information is very important for modeling real world domains. Recently, the DISPONTE semantics has been proposed for probabilistic description logics. In DISPONTE, the axioms of a knowledge base can be annotated with a set of variables and a real number between 0 and 1. This real number represents the probability of each version of the axiom in which the specified variables are instantiated. In this paper we present the algorithm BUNDLE for computing the probability of queries from DISPONTE knowledge bases that follow the ALC semantics. BUNDLE exploits an underlying DL reasoner, such as Pellet, that is able to return explanations for queries. The explanations are encoded in a Binary Decision Diagram from which the probability of the query is computed. The experiments performed by applying BUNDLE to probabilistic knowledge bases show that it can handle ontologies of realistic size and is competitive with the system PRONTO for the probabilistic description logic P-SHIQ(D).
%0 Book Section
%1 RiguzziBellodiEtAl13RR
%A Riguzzi, Fabrizio
%A Bellodi, Elena
%A Lamma, Evelina
%A Zese, Riccardo
%B Web Reasoning and Rule Systems: 7th International Conference, RR 2013, Mannheim, Germany
%C Heidelberg
%D 2013
%E Faber, Wolfgang
%E Lembo, Domenico
%I Springer
%K 01624 springer paper ai knowledge processing rules web application adaptive logic ontology zzz.sw
%P 183--197
%R 10.1007/978-3-642-39666-3_14
%T BUNDLE: A Reasoner for Probabilistic Ontologies
%V 7994
%X Representing uncertain information is very important for modeling real world domains. Recently, the DISPONTE semantics has been proposed for probabilistic description logics. In DISPONTE, the axioms of a knowledge base can be annotated with a set of variables and a real number between 0 and 1. This real number represents the probability of each version of the axiom in which the specified variables are instantiated. In this paper we present the algorithm BUNDLE for computing the probability of queries from DISPONTE knowledge bases that follow the ALC semantics. BUNDLE exploits an underlying DL reasoner, such as Pellet, that is able to return explanations for queries. The explanations are encoded in a Binary Decision Diagram from which the probability of the query is computed. The experiments performed by applying BUNDLE to probabilistic knowledge bases show that it can handle ontologies of realistic size and is competitive with the system PRONTO for the probabilistic description logic P-SHIQ(D).
%@ 978-3-642-39665-6
@incollection{RiguzziBellodiEtAl13RR,
abstract = {Representing uncertain information is very important for modeling real world domains. Recently, the DISPONTE semantics has been proposed for probabilistic description logics. In DISPONTE, the axioms of a knowledge base can be annotated with a set of variables and a real number between 0 and 1. This real number represents the probability of each version of the axiom in which the specified variables are instantiated. In this paper we present the algorithm BUNDLE for computing the probability of queries from DISPONTE knowledge bases that follow the ALC semantics. BUNDLE exploits an underlying DL reasoner, such as Pellet, that is able to return explanations for queries. The explanations are encoded in a Binary Decision Diagram from which the probability of the query is computed. The experiments performed by applying BUNDLE to probabilistic knowledge bases show that it can handle ontologies of realistic size and is competitive with the system PRONTO for the probabilistic description logic P-SHIQ(D).},
added-at = {2017-04-09T14:46:14.000+0200},
address = {Heidelberg},
author = {Riguzzi, Fabrizio and Bellodi, Elena and Lamma, Evelina and Zese, Riccardo},
biburl = {https://www.bibsonomy.org/bibtex/2caaaefb9aaee9271a37dd838eacfb6fd/flint63},
booktitle = {Web Reasoning and Rule Systems: 7th International Conference, RR 2013, Mannheim, Germany},
crossref = {RR2013},
doi = {10.1007/978-3-642-39666-3_14},
editor = {Faber, Wolfgang and Lembo, Domenico},
file = {SpringerLink:2013/RiguzziBellodiEtAl13RR.pdf:PDF},
groups = {public},
interhash = {e09db9b606080a8d8a30b5515c46cf42},
intrahash = {caaaefb9aaee9271a37dd838eacfb6fd},
isbn = {978-3-642-39665-6},
issn = {0302-9743},
keywords = {01624 springer paper ai knowledge processing rules web application adaptive logic ontology zzz.sw},
pages = {183--197},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
timestamp = {2017-07-13T17:15:23.000+0200},
title = {{BUNDLE:} A Reasoner for Probabilistic Ontologies},
username = {flint63},
volume = 7994,
year = 2013
}