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Proceedings of DL4KG2019 - Workshop
   on Deep Learning for Knowledge
                Graphs

                        Co-located with ESWC 2019
                  16th European Semantic Web Conference
                             Portoroz, Slovenia
                               2nd June 2019



                                 Edited by

                              Mehwish Alam *
                             Davide Buscaldi +
                             Michael Cochez †
                            Francesco Osborne 
                        Diego Reforgiato Recupero ⊕
                               Harald Sack *




∗ FIZ Karlsruhe - Leibniz Institute for Information Infrastructure, Germany
+ Labortoire d’Informatique Paris Nord (LIPN), Paris, France
† Fraunhofer Institute for Applied Information Technology FIT, Germany
 Knowledge Media Institute (KMI), The Open University, UK
⊕ University of Cagliari, Cagliari, Italy




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.
Proceedings submitted to CEUR-WS.org
Organizing Committee
 • Mehwish Alam, FIZ Karlsruhe - Leibniz Institute for Information Infras-
   tructure, Germany
 • Davide Buscaldi, Universitè Paris 13, USPC, Paris, France
 • Michael Cochez, Fraunhofer Institute for Applied Information Technology
   FIT, Germany

 • Francesco Osborne, Knowledge Media Institute (KMi), The Open Univer-
   sity, UK
 • Diego Reforgiato Recupero, University of Cagliari, Cagliari, Italy

 • Harald Sack, FIZ Karlsruhe - Leibniz Institute for Information Infrastruc-
   ture, Germany


Program Committee
 • Danilo Dessi, University of Cagliari, Italy

 • Stefan Dietze, L3S Hannover, Germany
 • Mauro Dragoni, Fondazione Bruno Kessler, Italy
 • Aldo Gangemi, University of Bologna, Italy

 • Pascal Hitzler, Wright State University, USA
 • Gerard de Melo, Rutgers University, USA
 • Amedeo Napoli, LORIA, CNRS, France
 • Finn Arup Nielsen, Technical University of Denmark, Denmark

 • Andrea Nuzzolese, , National Council of Research, Italy
 • Achim Rettinger, AIFB-KIT, Germany
 • Petar Ristoski, IBM research, USA

 • Thiviyan Thanapalasingam, The Open University, UK
 • Veronika Thost, IBM Research, USA
 • Volker Tresp, Siemens AG, Germany
Preface
Over the past years there has been a rapid growth in the use and the impor-
tance of Knowledge Graphs (KGs) along with their application to many impor-
tant tasks. KGs are large networks of real-world entities described in terms of
their semantic types and their relationships to each other. On the other hand,
Deep Learning methods have also become an important area of research, achiev-
ing some important breakthrough in various research fields, especially Natural
Language Processing (NLP) and Image Recognition.
    In order to pursue more advanced methodologies, it has become critical that
the communities related to Deep Learning, Knowledge Graphs, and NLP join
their forces in order to develop more effective algorithms and applications. This
workshop, in the wake of other similar efforts at previous Semantic Web con-
ferences such as ESWC2018 as DL4KGs and ISWC2018, aimed to reinforce the
relationships between these communities and foster inter-disciplinary research
in the areas of KG, Deep Learning, and Natural Language Processing.
Contents
Loss Functions in Knowledge Graph Embedding Models.
Sameh Mohamed, Vit Novacek, Pierre-Yves Vandenbussche and Emir Munoz
.............................................................................1

Graph-Convolution-Based Classification for Ontology Alignment Change
Prediction.
Matthias Jurisch and Bodo Igler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Mining Scholarly Data for Fine-Grained Knowledge Graph Construc-
tion.
Davide Buscaldi, Danilo Dessi, Enrico Motta, Francesco Osborne and Diego
Reforgiato Recupero . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

A Comprehensive Survey of Knowledge Graph Embeddings with Lit-
erals: Techniques and Applications.
Genet Asefa Gesese, Russa Biswas and Harald Sack . . . . . . . . . . . . . . . . . . . . . 31

Iterative Entity Alignment with Improved Neural Attribute Embed-
ding.
Ning Pang, Weixin Zeng, Jiuyang Tang, Zhen Tan and Xiang Zhao . . . . . . 41

Knowledge Reconciliation with Graph Convolutional Networks: Pre-
liminary Results.
Pierre Monnin, Chedy Raissi, Amedeo Napoli and Adrien Coulet . . . . . . . . . 47

End-to-End Learning for Answering Structured Queries Directly over
Text.
Paul Groth, Antony Scerri, Ron Daniel and Bradley Allen . . . . . . . . . . . . . . . 57

Can Knowledge Graphs and Deep Learning Approaches help in Rep-
resenting, Detecting and Interpreting Metaphors?
Mehwish Alam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

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