Resources for Graph Data and Knowledge

Hogan, Aidan; Horrocks, Ian; Hotho, Andreas; Kagal, Lalana; Sattler, Uli

doi:10.4230/TGDK.2.2.1

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Resources for Graph Data and Knowledge

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

Part of: Issue: TGDK, Volume 2, Issue 2
Part of: Volume: TGDK, Volume 2
Part of: Journal: Transactions on Graph Data and Knowledge (TGDK)
License: Creative Commons Attribution 4.0 International license
Publication Date: 2024-12-18

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Document Identifiers

DOI: 10.4230/TGDK.2.2.1
URN: urn:nbn:de:0030-drops-225851

Subject Classification

ACM Subject Classification

Computing methodologies → Knowledge representation and reasoning
Information systems → Semantic web description languages
Information systems → Graph-based database models
Computing methodologies → Machine learning
Theory of computation → Graph algorithms analysis
Mathematics of computing → Graph theory

Keywords

Graphs
Data
Knowledge

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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 Get BibTex

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) https://doi.org/10.4230/TGDK.2.2.1

Author Details

Aidan Hogan

DCC, Universidad de Chile, IMFD, Chile

Ian Horrocks

University of Oxford, U.K.

Andreas Hotho

Department of Informatics, University of Würzburg, Germany

Lalana Kagal

Massachusetts Institute of Technology, Cambridge, MA, USA

Uli Sattler

University of Manchester, U.K.

Acknowledgements

We warmly thank Dagstuhl Publishing for their continued collaboration, the Semantic Web Science Association (SWSA) for their support, our colleagues on the SWSA Task Force who helped to plan this new journal, as well as our Advisory and Editorial Boards for their contributions towards getting the journal up and running and ensuring its continued operation and development.

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