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20182023
most citedMAP Inference for Probabilistic Logic Programming

8 citations · 18 across the 6 of their papers we have counts for

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cs.AI2023

Exploiting Uncertainty for Querying Inconsistent Description Logics Knowledge Bases

Riccardo Zese, Evelina Lamma, Fabrizio Riguzzi

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…

cs.AI2021

Nonground Abductive Logic Programming with Probabilistic Integrity Constraints

Elena Bellodi, Marco Gavanelli, Riccardo Zese +2

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 hypothetica…

cs.AI2020★ 2 cited

A Framework for Reasoning on Probabilistic Description Logics

Giuseppe Cota, Riccardo Zese, Elena Bellodi +2

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-probabili…

cs.AI2020★ 8 cited

MAP Inference for Probabilistic Logic Programming

Elena Bellodi, Marco Alberti, Fabrizio Riguzzi +1

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…

cs.AI2018★ 7 cited

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

Riccardo Zese, Giuseppe Cota, Evelina Lamma +2

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 un…