Zobrazeno 1 - 10
of 90
pro vyhledávání: '"Zese, Riccardo"'
The necessity to manage inconsistency in Description Logics Knowledge Bases (KBs) has come to the fore with the increasing importance gained by the Semantic Web, where information comes from different sources that constantly change their content and
Externí odkaz:
http://arxiv.org/abs/2306.09138
Publikováno v:
EPTCS 364, 2022, pp. 65-78
Hybrid Knowledge Bases based on Lifschitz's logic of Minimal Knowledge with Negation as Failure are a successful approach to combine the expressivity of Description Logics and Logic Programming in a single language. Their syntax, defined by Motik and
Externí odkaz:
http://arxiv.org/abs/2208.03092
Publikováno v:
Theory and Practice of Logic Programming, 21(5), 557-574, 2021
Uncertain information is being taken into account in an increasing number of application fields. In the meantime, abduction has been proved a powerful tool for handling hypothetical reasoning and incomplete knowledge. Probabilistic logical models are
Externí odkaz:
http://arxiv.org/abs/2108.03033
Autor:
Gentili, Elisabetta, Franchini, Giorgia, Zese, Riccardo, Alberti, Marco, Ferrara, Maria, Domenicano, Ilaria, Grassi, Luigi
Publikováno v:
In Computer Methods and Programs in Biomedicine Update 2024 5
While there exist several reasoners for Description Logics, very few of them can cope with uncertainty. BUNDLE is an inference framework that can exploit several OWL (non-probabilistic) reasoners to perform inference over Probabilistic Description Lo
Externí odkaz:
http://arxiv.org/abs/2010.01087
Publikováno v:
Theory and Practice of Logic Programming 20 (2020) 641-655
In Probabilistic Logic Programming (PLP) the most commonly studied inference task is to compute the marginal probability of a query given a program. In this paper, we consider two other important tasks in the PLP setting: the Maximum-A-Posteriori (MA
Externí odkaz:
http://arxiv.org/abs/2008.01394
Publikováno v:
Theory and Practice of Logic Programming, 19 (3), 449-476, 2019
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 probab
Externí odkaz:
http://arxiv.org/abs/1809.06180
Autor:
Zese, Riccardo
The management of uncertainty in the Semantic Web is of foremost importance given the nature and origin of the available data. This book presents a probabilistic semantics for knowledge bases, DISPONTE, which is inspired by the distribution semantics
Publikováno v:
In International Journal of Approximate Reasoning March 2022 142:41-63
Publikováno v:
Theory and Practice of Logic Programming 14 (2014) 681-695
Lifted inference has been proposed for various probabilistic logical frameworks in order to compute the probability of queries in a time that depends on the size of the domains of the random variables rather than the number of instances. Even if vari
Externí odkaz:
http://arxiv.org/abs/1405.3218