Vol-3707
urn:nbn:de:0074-3707-4

Copyright © 2024 for the individual papers by the papers' authors. Copyright © 2024 for the volume as a collection by its editors. This volume and its papers are published under the Creative Commons License Attribution 4.0 International (CC BY 4.0).

D2R2 2024
Linked Data-driven Resilience Research 2024

Proceedings of the Third International Workshop on Linked Data-driven Resilience Research (D2R2’24) co-located with European Semantic Web Conference 2024 (ESWC 2024)

Hersonissos, Greece, May 27, 2024.

Edited by

Julia Holze * *****
Sebastian Tramp * ****
Michael Martin *
Sören Auer *
Ricardo Usbeck **
Nenad Krdzavac

* eccenca GmbH, Hainstraße 8, 04109 Leipzig, Germany
** Leuphana University Lüneburg, Germany
*** Institute for Applied Informatics, Goerdelerring 9, 04109 Leipzig, Germany
**** Technische Informationsbibliothek (TIB), Welfengarten 1 B, 30167 Hannover, Germany
***** Gottfried Wilhelm Leibniz University Hannover
****** AKSW Research Group
******* DBpedia Association
******** Chemnitz University of Technology, Germany

Table of Contents

Preface
Summary: There were 9 papers submitted for peer-review to this workshop. Out of these, 7 papers were accepted for this volume.

Session I

Empowering Supply Chains Resilience: LLMs-Powered BN for Proactive Supply Chain Risk Identification
Maryam Shahsavari, Omar Khadeer Hussain, Morteza Saberi, Pankaj Sharma
Anticipate Risk with the Value and Trade Flows Knowledge Graph
Felix Engel, Mark Vanin, Nenad Krdzavac
Entity Alignment for Knowledge Graphs in the Context of Supply Chain Risk Management
Rebeka Gadzo, Yushan Liu

Session II

Leveraging Small Language Models for Text2SPARQL Tasks to Improve the Resilience of AI Assistance
Felix Brei, Johannes Frey, Lars-Peter Meyer
Towards a Regional Public Dashboard for Crisis and Resilience Management
Fatih Kılıç, Till Grabo, Julia Lücke, Norman Radtke, Christian Danne, Sabine Gründer-Fahrer, Michael Martin
An Automated Evaluation Framework for Graph Database Query Generation Leveraging Large Language Models
Bailan He, Yushan Liu, Marcel Hildebrandt, Zifeng Ding, Yaomengxi Han, Volker Tresp
Towards Modeling the Structure of Product Dependencies in Supply Networks to Identify Bottlenecks Among Suppliers
Daniel Henselmann, Andreas Harth

2024-06-06: submitted by Nenad Krdzavac, metadata incl. bibliographic data published under Creative Commons CC0
2024-06-12: published on CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073) |valid HTML5|