| Vol-2377 urn:nbn:de:0074-2377-1 Copyright © 2019 for the individual papers by the papers' authors. Copying permitted for private and academic purposes. This volume is published and copyrighted by its editors.

# DL4KG2019
Workshop on Deep Learning for Knowledge Graphs

### Proceedings of the Workshop on Deep Learning for Knowledge Graphs (DL4KG2019)
Co-located with the 16th Extended Semantic Web Conference 2019 (ESWC 2019)

### Portoroz, Slovenia, June 2, 2019.

Edited by

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

* FIZ Karlsruhe, Leibniz Institute for Information Infrastructure, Germany
** Université Paris 13, Labortoire d’Informatique Paris Nord (LIPN), Paris, France
*** FIT, Fraunhofer Institute for Applied Information Technology, Germany
**** KMI, Knowledge Media Institute, The Open University, UK
***** University of Cagliari, Department of Mathematics and Computer Science, Cagliari, Italy

## Table of Contents

- Preface

### Papers

- Loss Functions in Knowledge Graph Embedding Models 1-10
  Sameh Mohamed, Vit Novacek, Pierre-Yves Vandenbussche, Emir Munoz
- Graph-Convolution-Based Classification for Ontology Alignment Change Prediction 11-20
  Matthias Jurisch, Bodo Igler
- Mining Scholarly Data for Fine-Grained Knowledge Graph Construction 21-30
  Davide Buscaldi, Danilo Dessi, Enrico Motta, Francesco Osborne, Diego Reforgiato Recupero
- A Comprehensive Survey of Knowledge Graph Embeddings with Literals: Techniques and Applications 31-40
  Genet Asefa Gesese, Russa Biswas, Harald Sack
- Iterative Entity Alignment with Improved Neural Attribute Embedding 41-46
  Ning Pang, Weixin Zeng, Jiuyang Tang, Zhen Tan, Xiang Zhao
- Knowledge Reconciliation with Graph Convolutional Networks: Preliminary Results 47-56
  Pierre Monnin, Chedy Raissi, Amedeo Napoli, Adrien Coulet
- End-to-End Learning for Answering Structured Queries Directly over Text 57-70
  Paul Groth, Antony Scerri, Ron Daniel, Bradley Allen
- Can Knowledge Graphs and Deep Learning Approaches help in Representing, Detecting and Interpreting Metaphors? 71-73
  Mehwish Alam

2019-06-11: submitted by Diego Reforgiato Recupero, metadata incl. bibliographic data published under Creative Commons CC0
2019-06-11: published on CEUR-WS.org |valid HTML5|