Approaches for performing inference from probabilistic ontologies following the DISPONTE semantics are discussed and the algorithm BUNDLE is presented for computing the probability of queries.Expand

This paper presents the algorithm BUNDLE for computing the probability of queries from DISPONTE knowledge bases, which exploits an underlying DL reasoner, such as Pellet, that is able to return explanations for queries.Expand

In DISPONTE the axioms of a probabilistic ontology can be annotated with an epistemic or a statistical probability, while the statistical probability considers the populations to which the axiom is applied.Expand

An overview of PILP is presented and the main results are discussed, showing how structure learning systems use parameter learning as a subroutine to improve the quality of their results.Expand

This work shows that Abductive Logic Programming (ALP) is also a suitable framework for representing Datalog± ontologies, supporting query answering through an abductive proof procedure, and smoothly achieving the integration of ontologies and rulebased reasoning.Expand

TRLL and TRILLP can be used to compute the probability of queries to knowledge bases following DISPONTE semantics and experiments comparing these with other systems show the feasibility of the approach.Expand

It is shown that Probabilistic Logic Programming (PLP) is a suitable approach for NLP in various scenarios and is used for cplint on SWISH, a web application for Probabilism Logic Programming.Expand

cplint on SWISH, a web interface to cplint, allows users to experiment with Probabilistic Logic Programming without the need to install a system, a procedure that is often complex, error prone, and limited mainly to the Linux platform.Expand

Advances and new features of cplint on SWISH are reported, including the capability of drawing the binary decision diagrams created during the inference processes, and the system now allows hybrid programs, i.e., programs where some of the random variables are continuous.Expand

The algorithm BUNDLE is presented, which exploits an underlying DL reasoner, such as Pellet, that is able to return explanations for queries, and can handle ontologies of realistic size and is competitive with the system PRONTO for the probabilistic description logic P-$\mathcal{SHIQ}$(D).Expand