5 Search Results for "Sarasua, Cristina"

Document
Research
DOI: 10.4230/TGDK.3.3.4
Mining Inter-Document Argument Structures in Scientific Papers for an Argument Web

Authors: Florian Ruosch, Cristina Sarasua, and Abraham Bernstein

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

Abstract
In Argument Mining, predicting argumentative relations between texts (or spans) remains one of the most challenging aspects, even more so in the cross-document setting. This paper makes three key contributions to advance research in this domain. We first extend an existing dataset, the Sci-Arg corpus, by annotating it with explicit inter-document argumentative relations, thereby allowing arguments to be distributed over several documents forming an Argument Web; these new annotations are published using Semantic Web technologies (RDF, OWL). Second, we explore and evaluate three automated approaches for predicting these inter-document argumentative relations, establishing critical baselines on the new dataset. We find that a simple classifier based on discourse indicators with access to context outperforms neural methods. Third, we conduct a comparative analysis of these approaches for both intra- and inter-document settings, identifying statistically significant differences in results that indicate the necessity of distinguishing between these two scenarios. Our findings highlight significant challenges in this complex domain and open crucial avenues for future research on the Argument Web of Science, particularly for those interested in leveraging Semantic Web technologies and knowledge graphs to understand scholarly discourse. With this, we provide the first stepping stones in the form of a benchmark dataset, three baseline methods, and an initial analysis for a systematic exploration of this field relevant to the Web of Data and Science.
Cite as

Florian Ruosch, Cristina Sarasua, and Abraham Bernstein. Mining Inter-Document Argument Structures in Scientific Papers for an Argument Web. In Transactions on Graph Data and Knowledge (TGDK), Volume 3, Issue 3, pp. 4:1-4:33, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@Article{ruosch_et_al:TGDK.3.3.4,
  author =	{Ruosch, Florian and Sarasua, Cristina and Bernstein, Abraham},
  title =	{{Mining Inter-Document Argument Structures in Scientific Papers for an Argument Web}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{4:1--4:33},
  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.3.3.4},
  URN =		{urn:nbn:de:0030-drops-252159},
  doi =		{10.4230/TGDK.3.3.4},
  annote =	{Keywords: Argument Mining, Large Language Models, Knowledge Graphs, Link Prediction}
}
Document
Research
DOI: 10.4230/TGDK.3.1.2
Talking Wikidata: Communication Patterns and Their Impact on Community Engagement in Collaborative Knowledge Graphs

Authors: Elisavet Koutsiana, Ioannis Reklos, Kholoud Saad Alghamdi, Nitisha Jain, Albert Meroño-Peñuela, and Elena Simperl

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

Abstract
We study collaboration patterns of Wikidata, one of the world's largest open source collaborative knowledge graph (KG) communities. Collaborative KG communities, play a key role in structuring machine-readable knowledge to support AI systems like conversational agents. However, these communities face challenges related to long-term member engagement, as a small subset of contributors often is responsible for the majority of contributions and decision-making. While prior research has explored contributors' roles and lifespans, discussions within collaborative KG communities remain understudied. To fill this gap, we investigated the behavioural patterns of contributors and factors affecting their communication and participation. We analysed all the discussions on Wikidata using a mixed methods approach, including statistical tests, network analysis, and text and graph embedding representations. Our findings reveal that the interactions between Wikidata editors form a small world network, resilient to dropouts and inclusive, where both the network topology and discussion content influence the continuity of conversations. Furthermore, the account age of Wikidata members and their conversations are significant factors in their long-term engagement with the project. Our observations and recommendations can benefit the Wikidata and semantic web communities, providing guidance on how to improve collaborative environments for sustainability, growth, and quality.
Cite as

Elisavet Koutsiana, Ioannis Reklos, Kholoud Saad Alghamdi, Nitisha Jain, Albert Meroño-Peñuela, and Elena Simperl. Talking Wikidata: Communication Patterns and Their Impact on Community Engagement in Collaborative Knowledge Graphs. In Transactions on Graph Data and Knowledge (TGDK), Volume 3, Issue 1, pp. 2:1-2:27, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@Article{koutsiana_et_al:TGDK.3.1.2,
  author =	{Koutsiana, Elisavet and Reklos, Ioannis and Alghamdi, Kholoud Saad and Jain, Nitisha and Mero\~{n}o-Pe\~{n}uela, Albert and Simperl, Elena},
  title =	{{Talking Wikidata: Communication Patterns and Their Impact on Community Engagement in Collaborative Knowledge Graphs}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{2:1--2:27},
  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.3.1.2},
  URN =		{urn:nbn:de:0030-drops-230114},
  doi =		{10.4230/TGDK.3.1.2},
  annote =	{Keywords: collaborative knowledge graph, network analysis, graph embeddings, text embeddings}
}
Document
Survey
DOI: 10.4230/TGDK.1.1.11
How Does Knowledge Evolve in Open Knowledge Graphs?

Authors: Axel Polleres, Romana Pernisch, Angela Bonifati, Daniele Dell'Aglio, Daniil Dobriy, Stefania Dumbrava, Lorena Etcheverry, Nicolas Ferranti, Katja Hose, Ernesto Jiménez-Ruiz, Matteo Lissandrini, Ansgar Scherp, Riccardo Tommasini, and Johannes Wachs

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
Openly available, collaboratively edited Knowledge Graphs (KGs) are key platforms for the collective management of evolving knowledge. The present work aims t o provide an analysis of the obstacles related to investigating and processing specifically this central aspect of evolution in KGs. To this end, we discuss (i) the dimensions of evolution in KGs, (ii) the observability of evolution in existing, open, collaboratively constructed Knowledge Graphs over time, and (iii) possible metrics to analyse this evolution. We provide an overview of relevant state-of-the-art research, ranging from metrics developed for Knowledge Graphs specifically to potential methods from related fields such as network science. Additionally, we discuss technical approaches - and their current limitations - related to storing, analysing and processing large and evolving KGs in terms of handling typical KG downstream tasks.
Cite as

Axel Polleres, Romana Pernisch, Angela Bonifati, Daniele Dell'Aglio, Daniil Dobriy, Stefania Dumbrava, Lorena Etcheverry, Nicolas Ferranti, Katja Hose, Ernesto Jiménez-Ruiz, Matteo Lissandrini, Ansgar Scherp, Riccardo Tommasini, and Johannes Wachs. How Does Knowledge Evolve in Open Knowledge Graphs?. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 11:1-11:59, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@Article{polleres_et_al:TGDK.1.1.11,
  author =	{Polleres, Axel and Pernisch, Romana and Bonifati, Angela and Dell'Aglio, Daniele and Dobriy, Daniil and Dumbrava, Stefania and Etcheverry, Lorena and Ferranti, Nicolas and Hose, Katja and Jim\'{e}nez-Ruiz, Ernesto and Lissandrini, Matteo and Scherp, Ansgar and Tommasini, Riccardo and Wachs, Johannes},
  title =	{{How Does Knowledge Evolve in Open Knowledge Graphs?}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{11:1--11:59},
  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.1.1.11},
  URN =		{urn:nbn:de:0030-drops-194855},
  doi =		{10.4230/TGDK.1.1.11},
  annote =	{Keywords: KG evolution, temporal KG, versioned KG, dynamic KG}
}
Document
DOI: 10.4230/DagRep.12.9.1
Challenges and Opportunities of Democracy in the Digital Society (Dagstuhl Seminar 22361)

Authors: Abraham Bernstein, Anita Gohdes, Cristina Sarasua, Steffen Staab, and Beth Simone Noveck

Published in: Dagstuhl Reports, Volume 12, Issue 9 (2023)

Abstract
Digital technologies amplify and change societal processes. So far, society and intellectuals have painted two extremes of viewing the effects of the digital transformation on democratic life. While the early 2000s to mid-2010s declared the "liberating" aspects of digital technology, the post-Brexit events and the 2016 US elections have emphasized the "dark side" of the digital revolution. Now, explicit effort is needed to go beyond tech saviorism or doom scenarios. To this end, we organized the Dagstuhl Seminar 22361 "Challenges and Opportunities of Democracy in the Digital Society" to discuss the future of digital democracy. This report presents a summary of the seminar, which took place in Dagstuhl in September 2022. The seminar attracted scientific scholars from various disciplines, including political science, computer science, jurisprudence, and communication science, as well as civic technology practitioners.
Cite as

Abraham Bernstein, Anita Gohdes, Cristina Sarasua, Steffen Staab, and Beth Simone Noveck. Challenges and Opportunities of Democracy in the Digital Society (Dagstuhl Seminar 22361). In Dagstuhl Reports, Volume 12, Issue 9, pp. 1-19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@Article{bernstein_et_al:DagRep.12.9.1,
  author =	{Bernstein, Abraham and Gohdes, Anita and Sarasua, Cristina and Staab, Steffen and Noveck, Beth Simone},
  title =	{{Challenges and Opportunities of Democracy in the Digital Society (Dagstuhl Seminar 22361)}},
  pages =	{1--19},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2023},
  volume =	{12},
  number =	{9},
  editor =	{Bernstein, Abraham and Gohdes, Anita and Sarasua, Cristina and Staab, Steffen and Noveck, Beth Simone},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.12.9.1},
  URN =		{urn:nbn:de:0030-drops-178073},
  doi =		{10.4230/DagRep.12.9.1},
  annote =	{Keywords: co-design, democratic regulation, large-scale decision-making, large-scale deliberation, society}
}
Document
DOI: 10.4230/DagRep.4.7.25
Crowdsourcing and the Semantic Web (Dagstuhl Seminar 14282)

Authors: Abraham Bernstein, Jan Marco Leimeister, Natasha Noy, Cristina Sarasua, and Elena Simperl

Published in: Dagstuhl Reports, Volume 4, Issue 7 (2014)

Abstract
Semantic technologies provide flexible and scalable solutions to master and make sense of an increasingly vast and complex data landscape. However, while this potential has been acknowledged for various application scenarios and domains, and a number of success stories exist, it is equally clear that the development and deployment of semantic technologies will always remain reliant of human input and intervention. This is due to the very nature of some of the tasks associated with the semantic data management life cycle, which are famous for their knowledge-intensive and/or context-specific character; examples range from conceptual modeling in almost any flavor, to labeling resources (in different languages), describing their content in terms of ontological terms, or recognizing similar concepts and entities. For this reason, the Semantic Web community has always looked into applying the latest theories, methods and tools from CSCW (Computer Supported Cooperative Work), participatory design, Web 2.0, social computing, and, more recently crowdsourcing to find ways to engage with users and encourage their involvement in the execution of technical tasks. Existing approaches include the usage of wikis as semantic content authoring environments, leveraging folksonomies to create formal ontologies, but also human computation approaches such as games with a purpose or micro-tasks. This document provides a summary of the Dagstuhl Seminar 14282: Crowdsourcing and the Semantic Web, which in July 2014 brought together researchers of the emerging scientific community at the intersection of crowdsourcing and Semantic Web technologies. We collect the position statements written by the participants of seminar, which played a central role in the discussions about the evolution of our research field.
Cite as

Abraham Bernstein, Jan Marco Leimeister, Natasha Noy, Cristina Sarasua, and Elena Simperl. Crowdsourcing and the Semantic Web (Dagstuhl Seminar 14282). In Dagstuhl Reports, Volume 4, Issue 7, pp. 25-51, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2014)

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@Article{bernstein_et_al:DagRep.4.7.25,
  author =	{Bernstein, Abraham and Leimeister, Jan Marco and Noy, Natasha and Sarasua, Cristina and Simperl, Elena},
  title =	{{Crowdsourcing and the Semantic Web (Dagstuhl Seminar 14282)}},
  pages =	{25--51},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2014},
  volume =	{4},
  number =	{7},
  editor =	{Bernstein, Abraham and Leimeister, Jan Marco and Noy, Natasha and Sarasua, Cristina and Simperl, Elena},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.4.7.25},
  URN =		{urn:nbn:de:0030-drops-47845},
  doi =		{10.4230/DagRep.4.7.25},
  annote =	{Keywords: Crowdsourcing, Human Computation, Games with a Purpose, Microtask Crowdsourcing, Semantic Web, Linked Data, Quality Assurance, Crowd Management, Work Incentives}
}
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