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Proceedings submitted to CEUR-WS.org
• Mehwish Alam, FIZ Karlsruhe - Leibniz Institute for Information Infrastructure, Germany • Davide Buscaldi, Universite Paris 13, USPC, Paris, France • Michael Cochez, Fraunhofer Institute for Applied Information Technology
FIT, Germany • Francesco Osborne, Knowledge Media Institute (KMi), The Open University, UK • Diego Reforgiato Recupero, University of Cagliari, Cagliari, Italy • Harald Sack, FIZ Karlsruhe - Leibniz Institute for Information Infrastructure, Germany
• 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 Over the past years there has been a rapid growth in the use and the importance of Knowledge Graphs (KGs) along with their application to many important 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, achieving some important breakthrough in various research elds, 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 e ective algorithms and applications. This workshop, in the wake of other similar e orts at previous Semantic Web conferences 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. Loss Functions in Knowledge Graph Embedding Models.
Sameh Mohamed, Vit Novacek, Pierre-Yves Vandenbussche and Emir Munoz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Graph-Convolution-Based Classi cation for Ontology Alignment Change Prediction.
Matthias Jurisch and Bodo Igler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Mining Scholarly Data for Fine-Grained Knowledge Graph Construction.
Davide Buscaldi, Danilo Dessi, Enrico Motta, Francesco Osborne and Diego Reforgiato Recupero . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 A Comprehensive Survey of Knowledge Graph Embeddings with Literals: Techniques and Applications.
Genet Asefa Gesese, Russa Biswas and Harald Sack . . . . . . . . . . . . . . . . . . . . . 31 Iterative Entity Alignment with Improved Neural Attribute Embedding.
Ning Pang, Weixin Zeng, Jiuyang Tang, Zhen Tan and Xiang Zhao . . . . . . 41 Knowledge Reconciliation with Graph Convolutional Networks: Preliminary 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 Representing, Detecting and Interpreting Metaphors? Mehwish Alam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71