Ontology learning, particularly axiom learning, is a challenging task that focuses on building
expressive and decidable ontologies. The literature proposes several research efforts aimed …

The quest for acquiring a formal representation of the knowledge of a domain of interest has
attracted researchers with various backgrounds into a diverse field called ontology learning …

The internet and the development of the semantic web have created the opportunity to
provide structured legal data on the web. However, most legal information is in text. It is …

Knowledge graph entity typing (KGET) aims at inferring plausible types of entities in
knowledge graphs. Existing approaches to KGET focus on how to better encode the …

Although neural models have achieved remarkable performance, they still encounter doubts
due to the intransparency. To this end, model prediction explanation is attracting more and …

The Internet and the development of the semantic web have created the opportunity to
provide structured legal data on the web. However, most legal information is in text. It is …

Abstract Knowledge graphs (KGs) contain rich resources that represent human knowledge
in the world. There are mainly two kinds of reasoning techniques in knowledge graphs …

Given a target class T of an OWL 2 ontology, positive (and possibly negative) examples of T,
we address the problem of learning, viz. inducing, from the examples, fuzzy class inclusion …

We foresee robots that bootstrap knowledge representations and use them for classifying
relevant situations and making decisions based on future observations. Particularly for …

OWL ontologies are nowadays a quite popular way to describe structured knowledge in
terms of classes, relations among classes and class instances. In this paper, given an OWL …