| Vol-3559 urn:nbn:de:0074-3559-5 Copyright © 2023 for the individual papers by the papers' authors. Copyright © 2023 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).

# DL4KG 2023
Deep Learning for Knowledge Graphs 2023

### Proceedings of the Workshop on Deep Learning for Knowledge Graphs (DL4KG 2023)
co-located with the 21th International Semantic Web Conference (ISWC 2023)

### Athens, November 6-10, 2023.

Edited by

### Mehwish Alam *
Davide Buscaldi **
Michael Cochez ***
Francesco Osborne ****
Diego Reforgiato Recupero *****

* Télécom Paris, Institut Polytechnique de Paris, France
** Université Sorbonne Paris Nord, Labortoire d'Informatique Paris Nord (LIPN), Paris, France
*** KRR, Vrije Universiteit Amsterdam, the Netherlands
**** KMI, Knowledge Media Institute, The Open University, UK
***** University of Cagliari, Department of Mathematics and Computer Science, Cagliari, Italy

## 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.

### Papers

- Location Query Answering Using Box Embeddings
  Eleni Tsalapati, Markos Iliakis, Manolis Koubarakis
- Knowledge Graph Injection for Reinforcement Learning
  Robert Wardenga, Liubov Kovriguina, Dmitrii Pliukhin, Daniil Radyush, Ivan SmoliakoV, Yuan Xue, Henrik Müller, Aleksei Pismerov, Dmitry Mouromtsev, Daniel Kudenko
- Benchmarking the Abilities of Large Language Models for RDF Knowledge Graph Creation and Comprehension: How Well Do LLMs Speak Turtle?
  Johannes Frey, Lars-Peter Meyer, Natanael Arndt, Felix Brei, Kirill Bulert
- Enhancing Large Language Models with Knowledge Graphs for Classification Tasks in the Tourism Domain
  Andrea Cadeddu, Alessandro Chessa, Vincenzo De Leo, Gianni Fenu, Enrico Motta, Francesco Osborne, Diego Reforgiato, Angelo Salatino, Luca Secchi
- Universal Preprocessing Operators for Embedding Knowledge Graphs with Literals
  Patryk Preisner, Heiko Paulheim
- NNKGC: Improving Knowledge Graph Completion with Node Neighborhoods
  Irene Li, Boming Yang
- Enhancing Scholarly Understanding: A Comparison of Knowledge Injection Strategies in Large Language Models
  Vincenzo De Leo, Andrea Cadeddu, Alessandro Chessa, Gianni Fenu, Enrico Motta, Francesco Osborne, Diego Reforgiato, Angelo Salatino, Luca Secchi

2023-11-21: submitted by Michael Cochez, metadata incl. bibliographic data published under Creative Commons CC0
2023-11-21: published on CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073) |valid HTML5|