Abstract Information systems have to deal with an increasing amount of data that is
heterogeneous, unstructured, or incomplete. In order to align and complete data, systems …

In this paper we investigate the relationships between a multipreferential semantics for
defeasible reasoning in knowledge representation and a deep neural network model …

Representing uncertain information is crucial for modeling real world domains. In this paper
we present a technique for the integration of probabilistic information in Description Logics …

This paper addresses questions of universality related to ontological engineering, namely
aims at substantiating (negative) answers to the following three basic questions:(i) Is there a …

We propose a family of probabilistic description logics (DLs) that are derived in a principled
way from Halpern's probabilistic first-order logic. The resulting probabilistic DLs have a two …

We propose a framework for querying probabilistic instance data in the presence of an
OWL2 QL ontology, arguing that the interplay of probabilities and ontologies is fruitful in …

Statistical information is ubiquitous but drawing valid conclusions from it is prohibitively hard.
We explain how knowledge graph embeddings can be used to approximate probabilistic …

We address the problem of handling inconsistency in uncertain knowledge bases that are
specified in the lightweight fragments of description logics DL-Lite. More specifically, we …

A real world entity is unique, but it can have several representations due to various interests
or perspectives. In this paper, we are interested in the problem of multi-representation in …

Markov logic networks (MLNs) have proven to be useful tools for reasoning about
uncertainty in complex knowledge bases. In this paper, we extend MLNs with numerical …