1 code implementation • 5 Nov 2023 • Kristoffer Æsøy, Ana Ozaki
Machine learning models, and in particular language models, are being applied to various tasks that require reasoning.
no code implementations • 25 Oct 2023 • Camille Bourgaux, Ana Ozaki, Rafael Peñaloza
We define a provenance semantics for a language that encompasses several lightweight description logics and show its relationships with semantics that have been defined for ontologies annotated with a specific kind of annotation (such as fuzzy degrees).
no code implementations • 20 May 2023 • Sophie Blum, Raoul Koudijs, Ana Ozaki, Samia Touileb
We propose a new algorithm that aims at extracting the "tightest Horn approximation" of the target theory and that is guaranteed to terminate in exponential time (in the worst case) and in polynomial time if the target has polynomially many non-Horn examples.
1 code implementation • 25 Mar 2023 • Emilia Przybysz, Bimal Bhattarai, Cosimo Persia, Ana Ozaki, Ole-Christoffer Granmo, Jivitesh Sharma
Then, we show the correctness of our encoding and provide results for the properties: adversarial robustness, equivalence, and similarity of TsMs.
no code implementations • 14 Sep 2022 • Ana Ozaki, Anum Rehman, Philip Turk, Marija Slavkovik
Autonomous systems that operate in a shared environment with people need to be able to follow the rules of the society they occupy.
no code implementations • 2 Jun 2022 • Johanna Jøsang, Ricardo Guimarães, Ana Ozaki
We study the effectiveness of Knowledge Graph Embeddings (KGE) for knowledge graph (KG) completion with rule mining.
no code implementations • 1 Apr 2022 • Cosimo Persia, Ana Ozaki
We investigate the problem of extracting rules, expressed in Horn logic, from neural network models.
no code implementations • 27 Aug 2021 • Camille Bourgaux, Ana Ozaki, Jeff Z. Pan
It has been shown that convex geometric regions capture the so-called quasi-chained rules.
no code implementations • 2 Apr 2021 • Ana Ozaki
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.
no code implementations • 25 Mar 2021 • Ana Ozaki
Ontologies are a popular way of representing domain knowledge, in particular, knowledge in domains related to life sciences.
no code implementations • 17 Aug 2020 • Sabiha Tahrat, German Braun, Alessandro Artale, Marco Gario, Ana Ozaki
This paper investigates the feasibility of automated reasoning over temporal DL-Lite (TDL-Lite) knowledge bases (KBs).
no code implementations • 6 May 2020 • Cosimo Persia, Ana Ozaki
We investigate learnability of possibilistic theories from entailments in light of Angluin's exact learning model.
no code implementations • 21 Jan 2020 • Camille Bourgaux, Ana Ozaki, Rafael Peñaloza, Livia Predoiu
We address the problem of handling provenance information in ELHr ontologies.
no code implementations • 17 Nov 2019 • Ana Ozaki, Cosimo Persia, Andrea Mazzullo
', with A an arbitrary data instance and q and query in Q.
no code implementations • 1 Jun 2019 • Diego Calvanese, Davide Lanti, Ana Ozaki, Rafael Penaloza, Guohui Xiao
In particular, we investigate the problems of (i) deciding whether a provenance annotated OBDA instance entails a provenance annotated conjunctive query, and (ii) computing a polynomial representing the provenance of a query entailed by a provenance annotated OBDA instance.
no code implementations • 8 Feb 2019 • Ana Ozaki, Nicolas Troquard
We investigate the problem of learning description logic ontologies from entailments via queries, using epistemic reasoning.
no code implementations • 20 Sep 2017 • Boris Konev, Carsten Lutz, Ana Ozaki, Frank Wolter
We study the problem of learning description logic (DL) ontologies in Angluin et al.'s framework of exact learning via queries.
no code implementations • 10 Sep 2016 • Montserrat Hermo, Ana Ozaki
A major problem in computational learning theory is whether the class of formulas in conjunctive normal form (CNF) is efficiently learnable.