10 Search Results for "Hotho, Andreas"

Issue

Transactions on Graph Data and Knowledge, Volume 2, Issue 2

TGDK, Volume 2, Issue 2

Editors: Aidan Hogan, Ian Horrocks, Andreas Hotho, Lalana Kagal, and Uli Sattler

Special Issue on Resources for Graph Data and Knowledge

Issue

Transactions on Graph Data and Knowledge, Volume 1, Issue 1

TGDK, Volume 1, Issue 1

Editors: Aidan Hogan, Ian Horrocks, Andreas Hotho, and Lalana Kagal

Special Issue on Trends in Graph Data and Knowledge

Document
Survey
DOI: 10.4230/TGDK.3.2.1
Resilience in Knowledge Graph Embeddings

Authors: Arnab Sharma, N'Dah Jean Kouagou, and Axel-Cyrille Ngonga Ngomo

Published in: TGDK, Volume 3, Issue 2 (2025). Transactions on Graph Data and Knowledge, Volume 3, Issue 2

Abstract
In recent years, knowledge graphs have gained interest and witnessed widespread applications in various domains, such as information retrieval, question-answering, recommendation systems, amongst others. Large-scale knowledge graphs to this end have demonstrated their utility in effectively representing structured knowledge. To further facilitate the application of machine learning techniques, knowledge graph embedding models have been developed. Such models can transform entities and relationships within knowledge graphs into vectors. However, these embedding models often face challenges related to noise, missing information, distribution shift, adversarial attacks, etc. This can lead to sub-optimal embeddings and incorrect inferences, thereby negatively impacting downstream applications. While the existing literature has focused so far on adversarial attacks on KGE models, the challenges related to the other critical aspects remain unexplored. In this paper, we, first of all, give a unified definition of resilience, encompassing several factors such as generalisation, in-distribution generalization, distribution adaption, and robustness. After formalizing these concepts for machine learning in general, we define them in the context of knowledge graphs. To find the gap in the existing works on resilience in the context of knowledge graphs, we perform a systematic survey, taking into account all these aspects mentioned previously. Our survey results show that most of the existing works focus on a specific aspect of resilience, namely robustness. After categorizing such works based on their respective aspects of resilience, we discuss the challenges and future research directions.
Cite as

Arnab Sharma, N'Dah Jean Kouagou, and Axel-Cyrille Ngonga Ngomo. Resilience in Knowledge Graph Embeddings. In Transactions on Graph Data and Knowledge (TGDK), Volume 3, Issue 2, pp. 1:1-1:38, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@Article{sharma_et_al:TGDK.3.2.1,
  author =	{Sharma, Arnab and Kouagou, N'Dah Jean and Ngomo, Axel-Cyrille Ngonga},
  title =	{{Resilience in Knowledge Graph Embeddings}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{1:1--1:38},
  ISSN =	{2942-7517},
  year =	{2025},
  volume =	{3},
  number =	{2},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.3.2.1},
  URN =		{urn:nbn:de:0030-drops-248117},
  doi =		{10.4230/TGDK.3.2.1},
  annote =	{Keywords: Knowledge graphs, Resilience, Robustness}
}
Document
DOI: 10.4230/LIPIcs.FORC.2025.5
Pessimism Traps and Algorithmic Interventions

Authors: Avrim Blum, Emily Diana, Kavya Ravichandran, and Alexander Tolbert

Published in: LIPIcs, Volume 329, 6th Symposium on Foundations of Responsible Computing (FORC 2025)

Abstract
In this paper, we relate the philosophical literature on pessimism traps to information cascades, a formal model derived from the economics and mathematics literature. A pessimism trap is a social pattern in which individuals in a community, in situations of uncertainty, copy the sub-optimal actions of others, despite their individual beliefs. This maps nicely onto the concept of an information cascade, which involves a sequence of agents making a decision between two alternatives, with a private signal of the superior alternative and a public history of others' actions. Key results from the economics literature show that information cascades occur with probability one in many contexts, and depending on the strength of the signal, populations can fall into the incorrect cascade very easily and quickly. Once formed, in the absence of external perturbation, a cascade cannot be broken - therefore, we derive an intervention that can be used to nudge a population from an incorrect to a correct cascade and, importantly, maintain the cascade once the subsidy is discontinued. We extend this to the case of multiple communities, each of which might have a different optimal action, and a government providing subsidies that cannot discriminate between communities and does not know which action is optimal for each. We study this both theoretically and empirically.
Cite as

Avrim Blum, Emily Diana, Kavya Ravichandran, and Alexander Tolbert. Pessimism Traps and Algorithmic Interventions. In 6th Symposium on Foundations of Responsible Computing (FORC 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 329, pp. 5:1-5:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{blum_et_al:LIPIcs.FORC.2025.5,
  author =	{Blum, Avrim and Diana, Emily and Ravichandran, Kavya and Tolbert, Alexander},
  title =	{{Pessimism Traps and Algorithmic Interventions}},
  booktitle =	{6th Symposium on Foundations of Responsible Computing (FORC 2025)},
  pages =	{5:1--5:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-367-6},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{329},
  editor =	{Bun, Mark},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2025.5},
  URN =		{urn:nbn:de:0030-drops-231321},
  doi =		{10.4230/LIPIcs.FORC.2025.5},
  annote =	{Keywords: Pessimism trap, opinion dynamics, algorithmic interventions, subsidy, decision-making}
}
Document
Preface
DOI: 10.4230/TGDK.2.2.1
Resources for Graph Data and Knowledge

Authors: Aidan Hogan, Ian Horrocks, Andreas Hotho, Lalana Kagal, and Uli Sattler

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

Abstract
In this Special Issue of Transactions on Graph Data and Knowledge - entitled "Resources for Graph Data and Knowledge" - we present eight articles that describe key resources in the area. These resources cover a wide range of topics within the scope of the journal, including graph querying, graph learning, information extraction, and ontologies, addressing applications of knowledge graphs involving art, bibliographical metadata, research reproducibility, and transport networks.
Cite as

Aidan Hogan, Ian Horrocks, Andreas Hotho, Lalana Kagal, and Uli Sattler. Resources for Graph Data and Knowledge. In Special Issue on Resources for Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 2, Issue 2, pp. 1:1-1:2, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@Article{hogan_et_al:TGDK.2.2.1,
  author =	{Hogan, Aidan and Horrocks, Ian and Hotho, Andreas and Kagal, Lalana and Sattler, Uli},
  title =	{{Resources for Graph Data and Knowledge}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{1:1--1:2},
  ISSN =	{2942-7517},
  year =	{2024},
  volume =	{2},
  number =	{2},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.2.2.1},
  URN =		{urn:nbn:de:0030-drops-225851},
  doi =		{10.4230/TGDK.2.2.1},
  annote =	{Keywords: Graphs, Data, Knowledge}
}
Document
Vision
DOI: 10.4230/TGDK.1.1.6
Towards Ordinal Data Science

Authors: Gerd Stumme, Dominik Dürrschnabel, and Tom Hanika

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
Order is one of the main instruments to measure the relationship between objects in (empirical) data. However, compared to methods that use numerical properties of objects, the amount of ordinal methods developed is rather small. One reason for this is the limited availability of computational resources in the last century that would have been required for ordinal computations. Another reason - particularly important for this line of research - is that order-based methods are often seen as too mathematically rigorous for applying them to real-world data. In this paper, we will therefore discuss different means for measuring and ‘calculating’ with ordinal structures - a specific class of directed graphs - and show how to infer knowledge from them. Our aim is to establish Ordinal Data Science as a fundamentally new research agenda. Besides cross-fertilization with other cornerstone machine learning and knowledge representation methods, a broad range of disciplines will benefit from this endeavor, including, psychology, sociology, economics, web science, knowledge engineering, scientometrics.
Cite as

Gerd Stumme, Dominik Dürrschnabel, and Tom Hanika. Towards Ordinal Data Science. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 6:1-6:39, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@Article{stumme_et_al:TGDK.1.1.6,
  author =	{Stumme, Gerd and D\"{u}rrschnabel, Dominik and Hanika, Tom},
  title =	{{Towards Ordinal Data Science}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{6:1--6:39},
  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.1.1.6},
  URN =		{urn:nbn:de:0030-drops-194801},
  doi =		{10.4230/TGDK.1.1.6},
  annote =	{Keywords: Order relation, data science, relational theory of measurement, metric learning, general algebra, lattices, factorization, approximations and heuristics, factor analysis, visualization, browsing, explainability}
}
Document
Preface
DOI: 10.4230/TGDK.1.1.1
Transactions on Graph Data and Knowledge

Authors: Aidan Hogan, Ian Horrocks, Andreas Hotho, and Lalana Kagal

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
Transactions on Graph Data and Knowledge (TGDK) is a new journal publishing peer-reviewed research on graph-based abstractions for data and knowledge, as well as the techniques, theories, applications and results that arise in this setting. TGDK is a community-run, Diamond Open Access journal, meaning that papers are published openly on the Web without fees for authors or readers. In this preface, we provide some brief remarks about the rationale and goals of the new journal, followed by an introduction to its inaugural issue, entitled "Trends in Graph Data and Knowledge", which collects together 12 diverse vision, position and survey papers on the types of research topics that exemplify the scope of this new journal.
Cite as

Aidan Hogan, Ian Horrocks, Andreas Hotho, and Lalana Kagal. Transactions on Graph Data and Knowledge. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 1:1-1:4, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@Article{hogan_et_al:TGDK.1.1.1,
  author =	{Hogan, Aidan and Horrocks, Ian and Hotho, Andreas and Kagal, Lalana},
  title =	{{Transactions on Graph Data and Knowledge}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{1:1--1:4},
  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.1.1.1},
  URN =		{urn:nbn:de:0030-drops-194757},
  doi =		{10.4230/TGDK.1.1.1},
  annote =	{Keywords: Graphs, Data, Knowledge}
}
Document
Complete Issue
DOI: 10.4230/TGDK.1.1
TGDK, Volume 1, Issue 1, Complete Issue
Abstract
TGDK, Volume 1, Issue 1, Complete Issue
Cite as

Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1: Special Issue on Trends in Graph Data and Knowledge, pp. 1-398, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@Article{TGDK.1.1,
  title =	{{TGDK, Volume 1, Issue 1, Complete Issue}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{1--398},
  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},
  URN =		{urn:nbn:de:0030-drops-194738},
  doi =		{10.4230/TGDK.1.1},
  annote =	{Keywords: TGDK, Volume 1, Issue 1, Complete Issue}
}
Document
Front Matter
DOI: 10.4230/TGDK.1.1.0
Front Matter, Table of Contents, List of Authors
Abstract
Front Matter, Table of Contents, List of Authors
Cite as

Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1: Special Issue on Trends in Graph Data and Knowledge, pp. 0:i-0:x, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@Article{TGDK.1.1.0,
  title =	{{Front Matter, Table of Contents, List of Authors}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{0:i--0:x},
  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.1.1.0},
  URN =		{urn:nbn:de:0030-drops-194746},
  doi =		{10.4230/TGDK.1.1.0},
  annote =	{Keywords: Front Matter, Table of Contents, List of Authors}
}
Document
DOI: 10.4230/DagSemProc.08391.6
08391 Working Group Summary – Analyzing Tag Semantics Across Tagging Systems

Authors: Dominik Benz, Marko Grobelnik, Andreas Hotho, Robert Jäschke, Dunja Mladenic, Vito D. P. Servedio, Sergej Sizov, and Martin Szomszor

Published in: Dagstuhl Seminar Proceedings, Volume 8391, Social Web Communities (2008)

Abstract
The objective of our group was to exploit state-of-the-art Information Retrieval methods for finding associations and dependencies between tags, capturing and representing differences in tagging behavior and vocabulary of various folksonomies, with the overall aim to better understand the semantics of tags and the tagging process. Therefore we analyze the semantic content of tags in the Flickr and Delicious folksonomies. We find that: tag context similarity leads to meaningful results in Flickr, despite its narrow folksonomy character; the comparison of tags across Flickr and Delicious shows little semantic overlap, being tags in Flickr associated more to visual aspects rather than technological as it seems to be in Delicious; there are regions in the tag-tag space, provided with the cosine similarity metric, that are characterized by high density; the order of tags inside a post has a semantic relevance.
Cite as

Dominik Benz, Marko Grobelnik, Andreas Hotho, Robert Jäschke, Dunja Mladenic, Vito D. P. Servedio, Sergej Sizov, and Martin Szomszor. 08391 Working Group Summary – Analyzing Tag Semantics Across Tagging Systems. In Social Web Communities. Dagstuhl Seminar Proceedings, Volume 8391, pp. 1-15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)

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@InProceedings{benz_et_al:DagSemProc.08391.6,
  author =	{Benz, Dominik and Grobelnik, Marko and Hotho, Andreas and J\"{a}schke, Robert and Mladenic, Dunja and Servedio, Vito D. P. and Sizov, Sergej and Szomszor, Martin},
  title =	{{08391 Working Group Summary – Analyzing Tag Semantics Across Tagging Systems}},
  booktitle =	{Social Web Communities},
  pages =	{1--15},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2008},
  volume =	{8391},
  editor =	{Harith Alani and Steffen Staab and Gerd Stumme},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08391.6},
  URN =		{urn:nbn:de:0030-drops-17854},
  doi =		{10.4230/DagSemProc.08391.6},
  annote =	{Keywords: Social Web Communities, Folksonomy, Tag, Semantics}
}
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