4 Search Results for "Shapiro, Steven"

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
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
DOI: 10.4230/DagSemProc.07351.10
Dynamic Interactions Between Goals and Beliefs

Authors: Steven Shapiro and Gerhard Brewka

Published in: Dagstuhl Seminar Proceedings, Volume 7351, Formal Models of Belief Change in Rational Agents (2007)

Abstract
Shapiro et al. [2005], presented a framework for representing goal change in the situation calculus. In that framework, agents adopt a goal when requested to do so (by some agent reqr), and they remain committed to the goal unless the request is cancelled by reqr. A common assumption in the agent theory literature, is that achievement goals that are believed to be impossible to achieve should be dropped. In this paper, we incorporate this assumption into Shapiro et al.'s framework, however we go a step further. If an agent believes a goal is impossible to achieve, it is dropped. However, if the agent later believes that it was mistaken about the impossibility of achieving the goal, the agent might readopt the goal. In addition, we consider an agent's goals as a whole when making them compatible with their beliefs, rather than considering them individually.
Cite as

Steven Shapiro and Gerhard Brewka. Dynamic Interactions Between Goals and Beliefs. In Formal Models of Belief Change in Rational Agents. Dagstuhl Seminar Proceedings, Volume 7351, pp. 1-9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2007)

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@InProceedings{shapiro_et_al:DagSemProc.07351.10,
  author =	{Shapiro, Steven and Brewka, Gerhard},
  title =	{{Dynamic Interactions Between Goals and Beliefs}},
  booktitle =	{Formal Models of Belief Change in Rational Agents},
  pages =	{1--9},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2007},
  volume =	{7351},
  editor =	{Giacomo Bonanno and James Delgrande and J\'{e}r\^{o}me Lang and Hans Rott},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.07351.10},
  URN =		{urn:nbn:de:0030-drops-11995},
  doi =		{10.4230/DagSemProc.07351.10},
  annote =	{Keywords: Goal Change, Belief Change, Situation Calculus}
}
Document
DOI: 10.4230/DagSemProc.05321.7
Belief Change with Noisy Sensing and Introspection

Authors: Steven Shapiro

Published in: Dagstuhl Seminar Proceedings, Volume 5321, Belief Change in Rational Agents: Perspectives from Artificial Intelligence, Philosophy, and Economics (2005)

Abstract
In this paper, we generalize the framework of Shapiro et al. [2000], where belief change due to sensing was combined with belief introspection in the situation calculus. In that framework, sensing was assumed to be infallible and the plausibilities of alternate situations (i.e., possible worlds) were fixed in the initial state, never to be updated. Here, we relax both assumptions. That is, we model noisy sensors whose readings may stray from reality and may return different values in subsequent readings. We also allow the plausibilities of situations to change over time, bringing the framework more in line with traditional models of belief change. We give some properties of our axiomatization and show that it does not suffer from the problems with combining sensing, introspection, and plausibility update described in Shapiro et al. [2000].
Cite as

Steven Shapiro. Belief Change with Noisy Sensing and Introspection. In Belief Change in Rational Agents: Perspectives from Artificial Intelligence, Philosophy, and Economics. Dagstuhl Seminar Proceedings, Volume 5321, pp. 1-6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)

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@InProceedings{shapiro:DagSemProc.05321.7,
  author =	{Shapiro, Steven},
  title =	{{Belief Change with Noisy Sensing and Introspection}},
  booktitle =	{Belief Change in Rational Agents: Perspectives from Artificial Intelligence, Philosophy, and Economics},
  pages =	{1--6},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2006},
  volume =	{5321},
  editor =	{James Delgrande and Jerome Lang and Hans Rott and Jean-Marc Tallon},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.05321.7},
  URN =		{urn:nbn:de:0030-drops-4632},
  doi =		{10.4230/DagSemProc.05321.7},
  annote =	{Keywords: Belief change, noisy sensing, theories of action, situation calculus.}
}
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