8 Search Results for "Peñaloza, Rafael"

Volume

OASIcs, Volume 99

International Research School in Artificial Intelligence in Bergen (AIB 2022)

AIB 2022, June 7-11, 2022, University of Bergen, Norway

Editors: Camille Bourgaux, Ana Ozaki, and Rafael Peñaloza

Document
Research
DOI: 10.4230/TGDK.3.3.1
A Logic Programming Approach to Repairing SHACL Constraint Violations

Authors: Shqiponja Ahmetaj, Robert David, Axel Polleres, and Mantas Šimkus

Published in: TGDK, Volume 3, Issue 3 (2025). Transactions on Graph Data and Knowledge, Volume 3, Issue 3

Abstract
The Shapes Constraint Language (SHACL) is a recent standard, a W3C recommendation, for validating RDF graphs against shape constraints to be checked on target nodes of a data graph. The standard also describes the notion of validation reports, which detail the results of the validation process. In case of violation of constraints, the validation report should explain the reasons for non-validation, offering guidance on how to identify or fix violations in the data graph. Since the specification left it open to SHACL processors to define such explanations, a recent work proposed the use of explanations in the style of database repairs, where a repair is a set of additions to or deletions from the data graph so that the resulting graph validates against the constraints. In this paper, we study such repairs for non-recursive SHACL, the largest fragment of SHACL that is fully defined in the specification. We propose an algorithm to compute repairs by encoding the explanation problem - using Answer Set Programming (ASP) - into a logic program, where the answer sets contain (minimal) repairs. We then study a scenario where it is not possible to simultaneously repair all the targets, which may be the case due to overall unsatisfiability or conflicting constraints. We introduce a relaxed notion of validation, which allows to validate a (maximal) subset of the targets and adapt the ASP translation to take into account this relaxation. Finally, we add support for repairing constraints which use property paths and equality of paths. Our implementation in clingo is - to the best of our knowledge - the first implementation of a repair program for SHACL.
Cite as

Shqiponja Ahmetaj, Robert David, Axel Polleres, and Mantas Šimkus. A Logic Programming Approach to Repairing SHACL Constraint Violations. In Transactions on Graph Data and Knowledge (TGDK), Volume 3, Issue 3, pp. 1:1-1:36, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@Article{ahmetaj_et_al:TGDK.3.3.1,
  author =	{Ahmetaj, Shqiponja and David, Robert and Polleres, Axel and \v{S}imkus, Mantas},
  title =	{{A Logic Programming Approach to Repairing SHACL Constraint Violations}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{1:1--1:36},
  ISSN =	{2942-7517},
  year =	{2025},
  volume =	{3},
  number =	{3},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.3.3.1},
  URN =		{urn:nbn:de:0030-drops-252124},
  doi =		{10.4230/TGDK.3.3.1},
  annote =	{Keywords: SHACL, Shapes Constraint Language, Database Repairs, Knowledge Graphs, Semantic Web, Answer Set Programming}
}
Document
Invited Paper
DOI: 10.4230/OASIcs.RW.2024/2025.6
Explaining Reasoning Results for Description Logic Ontologies (Invited Paper)

Authors: Patrick Koopmann

Published in: OASIcs, Volume 138, Joint Proceedings of the 20th and 21st Reasoning Web Summer Schools (RW 2024 & RW 2025)

Abstract
The Web Ontology Language (OWL), grounded in description logics, enables reasoning systems to infer implicit knowledge in a transparent manner. However, the expressivity of description logics and the complexity of large ontologies often results in reasoning outcomes that are hard to understand without additional tool support. Explanations of these outcomes are essential for users to understand ontology content, communicate its structure and behavior effectively, and debug undesired or missing inferences. This chapter provides an overview of the central explanation techniques that have been developed for explaining reasoning with description logic ontologies. Here, we consider both explanations for positive entailments (explaining why something can be deduced), as well as negative entailments (why something cannot be deduced). More specifically, we discuss justifications, proofs and interpolation as a means to explain positive entailments, and abduction for explaining negative entailments, where we also have a closer look at practical algorithms as well as practical and theoretical challenges.
Cite as

Patrick Koopmann. Explaining Reasoning Results for Description Logic Ontologies (Invited Paper). In Joint Proceedings of the 20th and 21st Reasoning Web Summer Schools (RW 2024 & RW 2025). Open Access Series in Informatics (OASIcs), Volume 138, pp. 6:1-6:29, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{koopmann:OASIcs.RW.2024/2025.6,
  author =	{Koopmann, Patrick},
  title =	{{Explaining Reasoning Results for Description Logic Ontologies}},
  booktitle =	{Joint Proceedings of the 20th and 21st Reasoning Web Summer Schools (RW 2024 \& RW 2025)},
  pages =	{6:1--6:29},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-405-5},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{138},
  editor =	{Artale, Alessandro and Bienvenu, Meghyn and Garc{\'\i}a, Yazm{\'\i}n Ib\'{a}\~{n}ez and Murlak, Filip},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.RW.2024/2025.6},
  URN =		{urn:nbn:de:0030-drops-250514},
  doi =		{10.4230/OASIcs.RW.2024/2025.6},
  annote =	{Keywords: Explanations, Justifications, Proofs, Craig Interpolation, Contrastive Explanations}
}
Document
Survey
DOI: 10.4230/TGDK.3.1.3
Uncertainty Management in the Construction of Knowledge Graphs: A Survey

Authors: Lucas Jarnac, Yoan Chabot, and Miguel Couceiro

Published in: TGDK, Volume 3, Issue 1 (2025). Transactions on Graph Data and Knowledge, Volume 3, Issue 1

Abstract
Knowledge Graphs (KGs) are a major asset for companies thanks to their great flexibility in data representation and their numerous applications, e.g., vocabulary sharing, Q&A or recommendation systems. To build a KG, it is a common practice to rely on automatic methods for extracting knowledge from various heterogeneous sources. However, in a noisy and uncertain world, knowledge may not be reliable and conflicts between data sources may occur. Integrating unreliable data would directly impact the use of the KG, therefore such conflicts must be resolved. This could be done manually by selecting the best data to integrate. This first approach is highly accurate, but costly and time-consuming. That is why recent efforts focus on automatic approaches, which represent a challenging task since it requires handling the uncertainty of extracted knowledge throughout its integration into the KG. We survey state-of-the-art approaches in this direction and present constructions of both open and enterprise KGs. We then describe different knowledge extraction methods and discuss downstream tasks after knowledge acquisition, including KG completion using embedding models, knowledge alignment, and knowledge fusion in order to address the problem of knowledge uncertainty in KG construction. We conclude with a discussion on the remaining challenges and perspectives when constructing a KG taking into account uncertainty.
Cite as

Lucas Jarnac, Yoan Chabot, and Miguel Couceiro. Uncertainty Management in the Construction of Knowledge Graphs: A Survey. In Transactions on Graph Data and Knowledge (TGDK), Volume 3, Issue 1, pp. 3:1-3:48, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@Article{jarnac_et_al:TGDK.3.1.3,
  author =	{Jarnac, Lucas and Chabot, Yoan and Couceiro, Miguel},
  title =	{{Uncertainty Management in the Construction of Knowledge Graphs: A Survey}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{3:1--3:48},
  ISSN =	{2942-7517},
  year =	{2025},
  volume =	{3},
  number =	{1},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.3.1.3},
  URN =		{urn:nbn:de:0030-drops-233733},
  doi =		{10.4230/TGDK.3.1.3},
  annote =	{Keywords: Knowledge reconciliation, Uncertainty, Heterogeneous sources, Knowledge graph construction}
}
Document
Complete Volume
DOI: 10.4230/OASIcs.AIB.2022
OASIcs, Volume 99, AIB 2022, Complete Volume

Authors: Camille Bourgaux, Ana Ozaki, and Rafael Peñaloza

Published in: OASIcs, Volume 99, International Research School in Artificial Intelligence in Bergen (AIB 2022)

Abstract
OASIcs, Volume 99, AIB 2022, Complete Volume
Cite as

International Research School in Artificial Intelligence in Bergen (AIB 2022). Open Access Series in Informatics (OASIcs), Volume 99, pp. 1-180, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@Proceedings{bourgaux_et_al:OASIcs.AIB.2022,
  title =	{{OASIcs, Volume 99, AIB 2022, Complete Volume}},
  booktitle =	{International Research School in Artificial Intelligence in Bergen (AIB 2022)},
  pages =	{1--180},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-228-0},
  ISSN =	{2190-6807},
  year =	{2022},
  volume =	{99},
  editor =	{Bourgaux, Camille and Ozaki, Ana and Pe\~{n}aloza, Rafael},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.AIB.2022},
  URN =		{urn:nbn:de:0030-drops-159976},
  doi =		{10.4230/OASIcs.AIB.2022},
  annote =	{Keywords: OASIcs, Volume 99, AIB 2022, Complete Volume}
}
Document
Front Matter
DOI: 10.4230/OASIcs.AIB.2022.0
Front Matter, Table of Contents, Preface, Conference Organization

Authors: Camille Bourgaux, Ana Ozaki, and Rafael Peñaloza

Published in: OASIcs, Volume 99, International Research School in Artificial Intelligence in Bergen (AIB 2022)

Abstract
Front Matter, Table of Contents, Preface, Conference Organization
Cite as

International Research School in Artificial Intelligence in Bergen (AIB 2022). Open Access Series in Informatics (OASIcs), Volume 99, pp. 0:i-0:x, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{bourgaux_et_al:OASIcs.AIB.2022.0,
  author =	{Bourgaux, Camille and Ozaki, Ana and Pe\~{n}aloza, Rafael},
  title =	{{Front Matter, Table of Contents, Preface, Conference Organization}},
  booktitle =	{International Research School in Artificial Intelligence in Bergen (AIB 2022)},
  pages =	{0:i--0:x},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-228-0},
  ISSN =	{2190-6807},
  year =	{2022},
  volume =	{99},
  editor =	{Bourgaux, Camille and Ozaki, Ana and Pe\~{n}aloza, Rafael},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.AIB.2022.0},
  URN =		{urn:nbn:de:0030-drops-159984},
  doi =		{10.4230/OASIcs.AIB.2022.0},
  annote =	{Keywords: Front Matter, Table of Contents, Preface, Conference Organization}
}
Document
Invited Paper
DOI: 10.4230/OASIcs.AIB.2022.1
Knowledge Graphs: A Guided Tour (Invited Paper)

Authors: Aidan Hogan

Published in: OASIcs, Volume 99, International Research School in Artificial Intelligence in Bergen (AIB 2022)

Abstract
Much has been written about knowledge graphs in the past years by authors coming from diverse communities. The goal of these lecture notes is to provide a guided tour to the secondary and tertiary literature concerning knowledge graphs where the reader can learn more about particular topics. In particular, we collate together brief summaries of relevant books, book collections, book chapters, journal articles and other publications that provide introductions, primers, surveys and perspectives regarding: knowledge graphs in general; graph data models and query languages; semantics in the form of graph schemata, ontologies and rules; graph theory, algorithms and analytics; graph learning, in the form of knowledge graph embeddings and graph neural networks; and the knowledge graph life-cycle, which incorporates works on constructing, refining and publishing knowledge graphs. Where available, we highlight and provide direct links to open access literature.
Cite as

Aidan Hogan. Knowledge Graphs: A Guided Tour (Invited Paper). In International Research School in Artificial Intelligence in Bergen (AIB 2022). Open Access Series in Informatics (OASIcs), Volume 99, pp. 1:1-1:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{hogan:OASIcs.AIB.2022.1,
  author =	{Hogan, Aidan},
  title =	{{Knowledge Graphs: A Guided Tour}},
  booktitle =	{International Research School in Artificial Intelligence in Bergen (AIB 2022)},
  pages =	{1:1--1:21},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-228-0},
  ISSN =	{2190-6807},
  year =	{2022},
  volume =	{99},
  editor =	{Bourgaux, Camille and Ozaki, Ana and Pe\~{n}aloza, Rafael},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.AIB.2022.1},
  URN =		{urn:nbn:de:0030-drops-159999},
  doi =		{10.4230/OASIcs.AIB.2022.1},
  annote =	{Keywords: knowledge graphs}
}
Document
DOI: 10.4230/DagRep.11.8.1
Extending the Synergies Between SAT and Description Logics (Dagstuhl Seminar 21361)

Authors: Joao Marques-Silva, Rafael Peñaloza, and Uli Sattler

Published in: Dagstuhl Reports, Volume 11, Issue 8 (2022)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 21361 "Extending the Synergies Between SAT and Description Logics". Propositional satisfiability (SAT) and description logics (DL) are two successful areas of computational logic where automated reasoning plays a fundamental role. While they share a common core (formalised on logic), the developments in both areas have diverged in their scopes, methods, and applications. The goal of this seminar was to reconnect the SAT and DL communities (understood in a broad sense) so that they can benefit from each other. The seminar thus focused on explaining the foundational principles, main results, and open problems of each area, and discussing potential avenues for collaborative progress.
Cite as

Joao Marques-Silva, Rafael Peñaloza, and Uli Sattler. Extending the Synergies Between SAT and Description Logics (Dagstuhl Seminar 21361). In Dagstuhl Reports, Volume 11, Issue 8, pp. 1-10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@Article{marquessilva_et_al:DagRep.11.8.1,
  author =	{Marques-Silva, Joao and Pe\~{n}aloza, Rafael and Sattler, Uli},
  title =	{{Extending the Synergies Between SAT and Description Logics (Dagstuhl Seminar 21361)}},
  pages =	{1--10},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2022},
  volume =	{11},
  number =	{8},
  editor =	{Marques-Silva, Joao and Pe\~{n}aloza, Rafael and Sattler, Uli},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.11.8.1},
  URN =		{urn:nbn:de:0030-drops-157661},
  doi =		{10.4230/DagRep.11.8.1},
  annote =	{Keywords: description logics, propositional satisfiability, reasoning services, standard and non-standard inferences}
}
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