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Documents authored by Vidal, Maria-Esther

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Position
DOI: 10.4230/TGDK.4.2.2
How Human-Centric Are Our Graph Data Abstractions?

Authors: Angela Bonifati, Anastasia Dimou, Stefania Dumbrava, George Fletcher, Katja Hose, George Konstantinidis, Jose Emilio Labra Gayo, Wim Martens, Nina Pardal, Liat Peterfreund, Katherine Thornton, Maria-Esther Vidal, and Hannes Voigt

Published in: TGDK, Volume 4, Issue 2 (2024): Special Issue on Data Management for (Knowledge) Graphs. Transactions on Graph Data and Knowledge, Volume 4, Issue 2

Abstract
Graph data abstractions are often assumed to be intuitive, but experience shows that they are not equally understandable or usable in practice. In this vision and challenges paper, we examine the human-centricity of contemporary graph data abstractions through four lenses: researchability, usability, teachability, and societal impact. Drawing on diverse real-world use cases, ranging from clinical data and collaborative knowledge bases to biological and pangenomic graphs, we distill insights from database research, human-computer interaction, and education. Based on this analysis, we identify open research challenges that must be addressed to make graph abstractions easier to study, use, learn, and reason about.
Cite as

Angela Bonifati, Anastasia Dimou, Stefania Dumbrava, George Fletcher, Katja Hose, George Konstantinidis, Jose Emilio Labra Gayo, Wim Martens, Nina Pardal, Liat Peterfreund, Katherine Thornton, Maria-Esther Vidal, and Hannes Voigt. How Human-Centric Are Our Graph Data Abstractions?. In Special Issue on Data Management for (Knowledge) Graphs. Transactions on Graph Data and Knowledge (TGDK), Volume 4, Issue 2, pp. 2:1-2:31, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@Article{bonifati_et_al:TGDK.4.2.2,
  author =	{Bonifati, Angela and Dimou, Anastasia and Dumbrava, Stefania and Fletcher, George and Hose, Katja and Konstantinidis, George and Gayo, Jose Emilio Labra and Martens, Wim and Pardal, Nina and Peterfreund, Liat and Thornton, Katherine and Vidal, Maria-Esther and Voigt, Hannes},
  title =	{{How Human-Centric Are Our Graph Data Abstractions?}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{2:1--2:31},
  ISSN =	{2942-7517},
  year =	{2026},
  volume =	{4},
  number =	{2},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.4.2.2},
  URN =		{urn:nbn:de:0030-drops-275837},
  doi =		{10.4230/TGDK.4.2.2},
  annote =	{Keywords: graph data abstractions, property graphs, RDF, human-centric data management, usability, computing education}
}
Document
Resource
DOI: 10.4230/TGDK.4.2.9
BLINKG: A Benchmark for LLM-Integrated Knowledge Graph Generation

Authors: Carla Castedo, Enrique Iglesias, Manuel Lama, Alberto Bugarín-Diz, Maria-Esther Vidal, and David Chaves-Fraga

Published in: TGDK, Volume 4, Issue 2 (2024): Special Issue on Data Management for (Knowledge) Graphs. Transactions on Graph Data and Knowledge, Volume 4, Issue 2

Abstract
Generating Knowledge Graphs (KGs) remains one of the most time-consuming and labor-intensive tasks for knowledge engineers, as they need to identify semantic equivalences between input data sources and ontology terms. While declarative solutions (e.g., RML, SPARQL-Anything) have helped to generalize this process, aligning input schema elements with ontology terms still involves intricate transformations and requires considerable manual effort. With the advent of Large Language Models (LLMs), there is growing interest in leveraging their capabilities to assist KG engineers. Although some studies have explored using LLMs to automate KG construction, there is still no standardized framework for assessing how effectively they establish correspondences between data schemes and ontology concepts. Therefore, in this paper, we propose BLINKG, a benchmark designed to evaluate the mapping capabilities of LLMs in constructing KGs from heterogeneous data sources. The benchmark includes a set of scenarios with increasing complexity, based on real-world use cases. We conduct an extensive experimental evaluation of several state-of-the-art LLMs using BLINK and observe that they already offer promising solutions. However, their performance remains limited in complex scenarios. Thanks to this benchmark we can already asses the current capabilities of LLMs for KG construction. Additionally, we define a set of requirements for achieving (semi)automated (LLM-driven) KG construction, opening new research lines in this area.
Cite as

Carla Castedo, Enrique Iglesias, Manuel Lama, Alberto Bugarín-Diz, Maria-Esther Vidal, and David Chaves-Fraga. BLINKG: A Benchmark for LLM-Integrated Knowledge Graph Generation. In Special Issue on Data Management for (Knowledge) Graphs. Transactions on Graph Data and Knowledge (TGDK), Volume 4, Issue 2, pp. 9:1-9:35, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@Article{castedo_et_al:TGDK.4.2.9,
  author =	{Castedo, Carla and Iglesias, Enrique and Lama, Manuel and Bugar{\'\i}n-Diz, Alberto and Vidal, Maria-Esther and Chaves-Fraga, David},
  title =	{{BLINKG: A Benchmark for LLM-Integrated Knowledge Graph Generation}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{9:1--9:35},
  ISSN =	{2942-7517},
  year =	{2026},
  volume =	{4},
  number =	{2},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.4.2.9},
  URN =		{urn:nbn:de:0030-drops-275901},
  doi =		{10.4230/TGDK.4.2.9},
  annote =	{Keywords: Knowledge Graph Construction, Benchmarking, Mapping Languages, Large Language Models}
}
Document
DOI: 10.4230/DagRep.15.1.136
Trust and Accountability in Knowledge Graph-Based AI for Self Determination (Dagstuhl Seminar 25051)

Authors: John Domingue, Luis-Daniel Ibáñez, Sabrina Kirrane, Maria-Esther Vidal, and Philipp D. Rohde

Published in: Dagstuhl Reports, Volume 15, Issue 1 (2025)

Abstract
This report documents the program and results of the Dagstuhl Seminar 25051 "Trust and Accountability in Knowledge Graph-Based AI for Self Determination". The seminar focused on AI systems powered by Knowledge Graphs and their fundamental role in powering intelligent decision making. Knowledge Graphs complement Machine Learning algorithms by providing data context and semantics, enabling further inference and question answering capabilities, and their synergy with Large Language Models is being actively researched. Despite the numerous benefits that can be accomplished with KG-based AI, its growing ubiquity within online services may raise the loss of self-determination for citizens as a fundamental societal issue. The more we rely on these technologies, which are often centralised, the less citizens will be able to determine their own destiny. To counter this threat, AI regulation, such as the EU AI Act, is being proposed in certain regions. Regulation sets what technologists need to do, leading to questions concerning: How can the output of AI systems be trusted? What is needed to ensure that the data fueling and the inner workings of these artefacts are transparent? How can AI be made accountable for its decision-making?
Cite as

John Domingue, Luis-Daniel Ibáñez, Sabrina Kirrane, Maria-Esther Vidal, and Philipp D. Rohde. Trust and Accountability in Knowledge Graph-Based AI for Self Determination (Dagstuhl Seminar 25051). In Dagstuhl Reports, Volume 15, Issue 1, pp. 136-200, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@Article{domingue_et_al:DagRep.15.1.136,
  author =	{Domingue, John and Ib\'{a}\~{n}ez, Luis-Daniel and Kirrane, Sabrina and Vidal, Maria-Esther and Rohde, Philipp D.},
  title =	{{Trust and Accountability in Knowledge Graph-Based AI for Self Determination (Dagstuhl Seminar 25051)}},
  pages =	{136--200},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2025},
  volume =	{15},
  number =	{1},
  editor =	{Domingue, John and Ib\'{a}\~{n}ez, Luis-Daniel and Kirrane, Sabrina and Vidal, Maria-Esther and Rohde, Philipp D.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.15.1.136},
  URN =		{urn:nbn:de:0030-drops-236722},
  doi =		{10.4230/DagRep.15.1.136},
  annote =	{Keywords: access control and privacy, federated query processing, intelligent knowledge graph management, programming paradigms for knowledge graphs, semantic data integration}
}
Document
DOI: 10.4230/DagRep.14.2.1
Are Knowledge Graphs Ready for the Real World? Challenges and Perspective (Dagstuhl Seminar 24061)

Authors: David Chaves-Fraga, Oscar Corcho, Anastasia Dimou, Maria-Esther Vidal, Ana Iglesias-Molina, and Dylan Van Assche

Published in: Dagstuhl Reports, Volume 14, Issue 2 (2024)

Abstract
This report documents the program and results of the Dagstuhl Seminar 24061 "Are Knowledge Graphs Ready for the Real World? Challenges and Perspectives". The seminar focused on gaining a better understanding of the open challenges required for the development of Knowledge Graph ecosystems. The seminar focused on four different topics: access control and privacy in decentralized knowledge graphs, knowledge graph construction lifecycle, software methods for improving KG implementation, and a new wave of knowledge engineers and their expected skills. By focusing on these relevant research topics, the seminar aimed to reflect on KGs from a more fundamental computer science perspective. It brought together interdisciplinary researchers from academia and industry to discuss foundations, concepts, and implementations that will pave the way for the next generation of KGs ready for real-world use.
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David Chaves-Fraga, Oscar Corcho, Anastasia Dimou, Maria-Esther Vidal, Ana Iglesias-Molina, and Dylan Van Assche. Are Knowledge Graphs Ready for the Real World? Challenges and Perspective (Dagstuhl Seminar 24061). In Dagstuhl Reports, Volume 14, Issue 2, pp. 1-70, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@Article{chavesfraga_et_al:DagRep.14.2.1,
  author =	{Chaves-Fraga, David and Corcho, Oscar and Dimou, Anastasia and Vidal, Maria-Esther and Iglesias-Molina, Ana and Van Assche, Dylan},
  title =	{{Are Knowledge Graphs Ready for the Real World? Challenges and Perspective (Dagstuhl Seminar 24061)}},
  pages =	{1--70},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2024},
  volume =	{14},
  number =	{2},
  editor =	{Chaves-Fraga, David and Corcho, Oscar and Dimou, Anastasia and Vidal, Maria-Esther and Iglesias-Molina, Ana and Van Assche, Dylan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.14.2.1},
  URN =		{urn:nbn:de:0030-drops-204983},
  doi =		{10.4230/DagRep.14.2.1},
  annote =	{Keywords: access control and privacy, federated query processing, intelligent knowledge graph management, programming paradigms for knowledge graphs, semantic data integration}
}
Document
Survey
DOI: 10.4230/TGDK.2.1.3
Semantic Web: Past, Present, and Future

Authors: Ansgar Scherp, Gerd Groener, Petr Škoda, Katja Hose, and Maria-Esther Vidal

Published in: TGDK, Volume 2, Issue 1 (2024): Special Issue on Trends in Graph Data and Knowledge - Part 2. Transactions on Graph Data and Knowledge, Volume 2, Issue 1

Abstract
Ever since the vision was formulated, the Semantic Web has inspired many generations of innovations. Semantic technologies have been used to share vast amounts of information on the Web, enhance them with semantics to give them meaning, and enable inference and reasoning on them. Throughout the years, semantic technologies, and in particular knowledge graphs, have been used in search engines, data integration, enterprise settings, and machine learning. In this paper, we recap the classical concepts and foundations of the Semantic Web as well as modern and recent concepts and applications, building upon these foundations. The classical topics we cover include knowledge representation, creating and validating knowledge on the Web, reasoning and linking, and distributed querying. We enhance this classical view of the so-called "Semantic Web Layer Cake" with an update of recent concepts that include provenance, security and trust, as well as a discussion of practical impacts from industry-led contributions. We conclude with an outlook on the future directions of the Semantic Web. This is a living document. If you like to contribute, please contact the first author and visit: https://github.com/ascherp/semantic-web-primer
Cite as

Ansgar Scherp, Gerd Groener, Petr Škoda, Katja Hose, and Maria-Esther Vidal. Semantic Web: Past, Present, and Future. In Special Issue on Trends in Graph Data and Knowledge - Part 2. Transactions on Graph Data and Knowledge (TGDK), Volume 2, Issue 1, pp. 3:1-3:37, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@Article{scherp_et_al:TGDK.2.1.3,
  author =	{Scherp, Ansgar and Groener, Gerd and \v{S}koda, Petr and Hose, Katja and Vidal, Maria-Esther},
  title =	{{Semantic Web: Past, Present, and Future}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{3:1--3:37},
  ISSN =	{2942-7517},
  year =	{2024},
  volume =	{2},
  number =	{1},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.2.1.3},
  URN =		{urn:nbn:de:0030-drops-198607},
  doi =		{10.4230/TGDK.2.1.3},
  annote =	{Keywords: Linked Open Data, Semantic Web Graphs, Knowledge Graphs}
}
Document
Vision
DOI: 10.4230/TGDK.1.1.9
Trust, Accountability, and Autonomy in Knowledge Graph-Based AI for Self-Determination

Authors: Luis-Daniel Ibáñez, John Domingue, Sabrina Kirrane, Oshani Seneviratne, Aisling Third, and Maria-Esther Vidal

Published in: TGDK, Volume 1, Issue 1 (2023): Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge, Volume 1, Issue 1

Abstract
Knowledge Graphs (KGs) have emerged as fundamental platforms for powering intelligent decision-making and a wide range of Artificial Intelligence (AI) services across major corporations such as Google, Walmart, and AirBnb. KGs complement Machine Learning (ML) algorithms by providing data context and semantics, thereby enabling further inference and question-answering capabilities. The integration of KGs with neuronal learning (e.g., Large Language Models (LLMs)) is currently a topic of active research, commonly named neuro-symbolic AI. Despite the numerous benefits that can be accomplished with KG-based AI, its growing ubiquity within online services may result in the loss of self-determination for citizens as a fundamental societal issue. The more we rely on these technologies, which are often centralised, the less citizens will be able to determine their own destinies. To counter this threat, AI regulation, such as the European Union (EU) AI Act, is being proposed in certain regions. The regulation sets what technologists need to do, leading to questions concerning How the output of AI systems can be trusted? What is needed to ensure that the data fuelling and the inner workings of these artefacts are transparent? How can AI be made accountable for its decision-making? This paper conceptualises the foundational topics and research pillars to support KG-based AI for self-determination. Drawing upon this conceptual framework, challenges and opportunities for citizen self-determination are illustrated and analysed in a real-world scenario. As a result, we propose a research agenda aimed at accomplishing the recommended objectives.
Cite as

Luis-Daniel Ibáñez, John Domingue, Sabrina Kirrane, Oshani Seneviratne, Aisling Third, and Maria-Esther Vidal. Trust, Accountability, and Autonomy in Knowledge Graph-Based AI for Self-Determination. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 9:1-9:32, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@Article{ibanez_et_al:TGDK.1.1.9,
  author =	{Ib\'{a}\~{n}ez, Luis-Daniel and Domingue, John and Kirrane, Sabrina and Seneviratne, Oshani and Third, Aisling and Vidal, Maria-Esther},
  title =	{{Trust, Accountability, and Autonomy in Knowledge Graph-Based AI for Self-Determination}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{9:1--9:32},
  ISSN =	{2942-7517},
  year =	{2023},
  volume =	{1},
  number =	{1},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.1.1.9},
  URN =		{urn:nbn:de:0030-drops-194839},
  doi =		{10.4230/TGDK.1.1.9},
  annote =	{Keywords: Trust, Accountability, Autonomy, AI, Knowledge Graphs}
}
Document
DOI: 10.4230/DagRep.7.6.135
Federated Semantic Data Management (Dagstuhl Seminar 17262)

Authors: Olaf Hartig, Maria-Esther Vidal, and Johann-Christoph Freytag

Published in: Dagstuhl Reports, Volume 7, Issue 6 (2018)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 17262 "Federated Semantic Data Management" (FSDM). The purpose of the seminar was to gather experts from the Semantic Web and Database communities, together with experts from application areas, to discuss in-depth open issues that have impeded FSDM approaches to be used on a large scale. The discussions were centered around the following four themes, each of which was the focus of a separate working group: i) graph data models, ii) federated query processing, iii) access control and privacy, and iv) use cases and applications. The main outcome of the seminar is a deeper understanding of the state of the art and of the open challenges of FSDM.
Cite as

Olaf Hartig, Maria-Esther Vidal, and Johann-Christoph Freytag. Federated Semantic Data Management (Dagstuhl Seminar 17262). In Dagstuhl Reports, Volume 7, Issue 6, pp. 135-167, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)

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@Article{hartig_et_al:DagRep.7.6.135,
  author =	{Hartig, Olaf and Vidal, Maria-Esther and Freytag, Johann-Christoph},
  title =	{{Federated Semantic Data Management (Dagstuhl Seminar 17262)}},
  pages =	{135--167},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2017},
  volume =	{7},
  number =	{6},
  editor =	{Hartig, Olaf and Vidal, Maria-Esther and Freytag, Johann-Christoph},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.7.6.135},
  URN =		{urn:nbn:de:0030-drops-82890},
  doi =		{10.4230/DagRep.7.6.135},
  annote =	{Keywords: Linked Data, Query Processing, RDF, SPARQL}
}

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