4 Search Results for "Fokkens, Antske"

Document
DOI: 10.4230/LIPIcs.GIScience.2025.16
What, When, and Where Do You Mean? Detecting Spatio-Temporal Concept Drift in Scientific Texts

Authors: Meilin Shi, Krzysztof Janowicz, Zilong Liu, Mina Karimi, Ivan Majic, and Alexandra Fortacz

Published in: LIPIcs, Volume 346, 13th International Conference on Geographic Information Science (GIScience 2025)

Abstract
Inundated by the rapidly expanding AI research nowadays, the research community requires more effective research data management than ever. A key challenge lies in the evolving nature of concepts embedded in the growing body of research publications. As concepts evolve over time (e.g., keywords like global warming become more commonly referred to as climate change), past research may become harder to find and interpret in a modern context. This phenomenon, known as concept drift, affects how research topics and keywords are understood, categorized, and retrieved. Beyond temporal drift, such variations also occur across geographic space, reflecting differences in local policies, research priorities, and so forth. In this work, we introduce the notion of spatio-temporal concept drift to capture how concepts in scientific texts evolve across both space and time. Using a scientometric dataset in geographic information science, we detect how research keywords drifted across countries and years using word embeddings. By detecting spatio-temporal concept drift, we can better align archival research and bridge regional differences, ensuring scientific knowledge remains findable and interoperable within evolving research landscapes.
Cite as

Meilin Shi, Krzysztof Janowicz, Zilong Liu, Mina Karimi, Ivan Majic, and Alexandra Fortacz. What, When, and Where Do You Mean? Detecting Spatio-Temporal Concept Drift in Scientific Texts. In 13th International Conference on Geographic Information Science (GIScience 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 346, pp. 16:1-16:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{shi_et_al:LIPIcs.GIScience.2025.16,
  author =	{Shi, Meilin and Janowicz, Krzysztof and Liu, Zilong and Karimi, Mina and Majic, Ivan and Fortacz, Alexandra},
  title =	{{What, When, and Where Do You Mean? Detecting Spatio-Temporal Concept Drift in Scientific Texts}},
  booktitle =	{13th International Conference on Geographic Information Science (GIScience 2025)},
  pages =	{16:1--16:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-378-2},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{346},
  editor =	{Sila-Nowicka, Katarzyna and Moore, Antoni and O'Sullivan, David and Adams, Benjamin and Gahegan, Mark},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2025.16},
  URN =		{urn:nbn:de:0030-drops-238450},
  doi =		{10.4230/LIPIcs.GIScience.2025.16},
  annote =	{Keywords: Concept Drift, Ontology, Large Language Models, Research Data Management}
}
Document
Vision
DOI: 10.4230/TGDK.1.1.3
Knowledge Engineering Using Large Language Models

Authors: Bradley P. Allen, Lise Stork, and Paul Groth

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 engineering is a discipline that focuses on the creation and maintenance of processes that generate and apply knowledge. Traditionally, knowledge engineering approaches have focused on knowledge expressed in formal languages. The emergence of large language models and their capabilities to effectively work with natural language, in its broadest sense, raises questions about the foundations and practice of knowledge engineering. Here, we outline the potential role of LLMs in knowledge engineering, identifying two central directions: 1) creating hybrid neuro-symbolic knowledge systems; and 2) enabling knowledge engineering in natural language. Additionally, we formulate key open research questions to tackle these directions.
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Bradley P. Allen, Lise Stork, and Paul Groth. Knowledge Engineering Using Large Language Models. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 3:1-3:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@Article{allen_et_al:TGDK.1.1.3,
  author =	{Allen, Bradley P. and Stork, Lise and Groth, Paul},
  title =	{{Knowledge Engineering Using Large Language Models}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{3:1--3:19},
  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.1.1.3},
  URN =		{urn:nbn:de:0030-drops-194777},
  doi =		{10.4230/TGDK.1.1.3},
  annote =	{Keywords: knowledge engineering, large language models}
}
Document
DOI: 10.4230/DagRep.12.5.37
Visual Text Analytics (Dagstuhl Seminar 22191)

Authors: Christopher Collins, Antske Fokkens, Andreas Kerren, Chris Weaver, and Angelos Chatzimparmpas

Published in: Dagstuhl Reports, Volume 12, Issue 5 (2022)

Abstract
Text data is one of the most abundant types of data available, produced every day across all domains of society. Understanding the contents of this data can support important policy decisions, help us understand society and culture, and improve business processes. While machine learning techniques are growing in their power for analyzing text data, there is still a clear role for human analysis and decision-making. This seminar explored the use of visual analytics applied to text data as a means to bridge the complementary strengths of people and computers. The field of visual text analytics applies visualization and interaction approaches which are tightly coupled to natural language processing systems to create analysis processes and systems for examining text and multimedia data. During the seminar, interdisciplinary working groups of experts from visualization, natural language processing, and machine learning examined seven topic areas to reflect on the state of the field, identify gaps in knowledge, and create an agenda for future cross-disciplinary research. This report documents the program and the outcomes of Dagstuhl Seminar 22191 "Visual Text Analytics".
Cite as

Christopher Collins, Antske Fokkens, Andreas Kerren, Chris Weaver, and Angelos Chatzimparmpas. Visual Text Analytics (Dagstuhl Seminar 22191). In Dagstuhl Reports, Volume 12, Issue 5, pp. 37-91, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@Article{collins_et_al:DagRep.12.5.37,
  author =	{Collins, Christopher and Fokkens, Antske and Kerren, Andreas and Weaver, Chris and Chatzimparmpas, Angelos},
  title =	{{Visual Text Analytics (Dagstuhl Seminar 22191)}},
  pages =	{37--91},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2022},
  volume =	{12},
  number =	{5},
  editor =	{Collins, Christopher and Fokkens, Antske and Kerren, Andreas and Weaver, Chris and Chatzimparmpas, Angelos},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.12.5.37},
  URN =		{urn:nbn:de:0030-drops-174432},
  doi =		{10.4230/DagRep.12.5.37},
  annote =	{Keywords: Information visualization, visual text analytics, visual analytics, text visualization, explainable ML for text analytics, language models, text mining, natural language processing}
}
Document
DOI: 10.4230/OASIcs.CMN.2014.241
Finding Stories in 1,784,532 Events: Scaling Up Computational Models of Narrative

Authors: Marieke van Erp, Antske Fokkens, and Piek Vossen

Published in: OASIcs, Volume 41, 2014 Workshop on Computational Models of Narrative

Abstract
Information professionals face the challenge of making sense of an ever increasing amount of information. Storylines can provide a useful way to present relevant information because they reveal explanatory relations between events. In this position paper, we present and discuss the four main challenges that make it difficult to get to these stories and our first ideas on how to start resolving them.
Cite as

Marieke van Erp, Antske Fokkens, and Piek Vossen. Finding Stories in 1,784,532 Events: Scaling Up Computational Models of Narrative. In 2014 Workshop on Computational Models of Narrative. Open Access Series in Informatics (OASIcs), Volume 41, pp. 241-245, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2014)

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@InProceedings{vanerp_et_al:OASIcs.CMN.2014.241,
  author =	{van Erp, Marieke and Fokkens, Antske and Vossen, Piek},
  title =	{{Finding Stories in 1,784,532 Events: Scaling Up Computational Models of Narrative}},
  booktitle =	{2014 Workshop on Computational Models of Narrative},
  pages =	{241--245},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-71-2},
  ISSN =	{2190-6807},
  year =	{2014},
  volume =	{41},
  editor =	{Finlayson, Mark A. and Meister, Jan Christoph and Bruneau, Emile G.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.CMN.2014.241},
  URN =		{urn:nbn:de:0030-drops-46601},
  doi =		{10.4230/OASIcs.CMN.2014.241},
  annote =	{Keywords: big data, news, aggregation, story detection}
}
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