Workshop at ESWC 2019 on
Deep Learning for Knowledge Graphs
More Details!

About

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 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 conferences such as ESWC2018 as DL4KGs and ISWC2018, aims to reinforce the relationships between these communities and foster inter-disciplinary research in the areas of KG, Deep Learning, and Natural Language Processing.


Topics of Interest

New approaches for combining Deep Learning and Knowledge Graphs
  • Methods for generating Knowledge Graph (node) embeddings
  • Scalability issues
  • Temporal Knowledge Graph Embeddings
  • Novel approaches
Applications of combining Deep Learning and Knowledge Graphs
  • Recommender Systems leveraging Knowledge Graphs
  • Link Prediction and completing KGs
  • Ontology Learning and Matching exploiting Knowledge Graph-Based Embeddings
  • Knowledge Graph-Based Sentiment Analysis
  • Natural Language Understanding/Machine Reading
  • Question Answering exploiting Knowledge Graphs and Deep Learning
  • Entity Linking
  • Trend Prediction based on Knowledge Graphs Embeddings
  • Domain Specific Knowledge Graphs (e.g., Scholarly, Biomedical, Musical)
  • Applying knowledge graph embeddings to real world scenarios.


Submission Details

Papers must comply with the LNCS style
Papers are submitted in PDF format via the workshop’s EasyChair submission pages

Submissions can fall in one of the following categories:
  • Full research papers (8-10 pages)
  • Short research papers (4-6 pages)
  • Position papers (2 pages)

Accepted papers (after blind review of at least 3 experts) will be published by CEUR–WS. The best paper (according to the reviewers’ rate) will be published within the main conference proceedings.

At least one of the authors of the accepted papers must register for the workshop (pre-conference only option) to be included into the workshop proceedings.

Important Dates

  • Friday March 1st, 2019  Friday March 7th, 2019 Friday March 15th, 2019: Full, Short and Position paper submission deadline
  • Friday March 29th, 2019 Friday April 11th, 2019: Notification of Acceptance
  • Friday April 12th, 2019 Monday May 20th, 2019: Camera-ready paper due
  • Sunday June 2nd, 2019: ESWC 2019 Workshop day

Keynote Speaker

Volker Tresp

Siemens, Munich, Germany

Title

Smart Perception with Deep Learning and Knowledge Graphs

Abstract

Following Goethe’s proverb, “you only see what you know”, we show how background knowledge formulated as Knowledge Graphs can dramatically improve information extraction from images by deep convolutional networks. We conclude that knowledge graph models, in connection with deep learning, can be the basis for many technical solutions requiring memory and perception, and might be a basis for modern AI.



Short Biography

Volker Tresp is Distinguished Research Scientist at Siemens Corporate Technology and Professor at the Ludwig Maximilian University in Munich. He received his Ph.D. degree from Yale University in 1989 and joined Siemens the same year. At Siemens he has been the head of various research teams in machine learning, data analytics and knowledge representation. He filed more than 70 patent applications, published more than 150 scientific articles, and was inventor of the year of Siemens in 1996. The company Panoratio is a spin-off out of his team. At the Ludwig Maximilian University he teaches an annual course on Machine Learning. His research focus in recent years has been “Machine Learning and Deep Learning with Information Networks” for modelling Knowledge Graphs, medical decision processes, perception, and cognitive memory functions.

Workshop Program and Proceedings

Accepted Papers.

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

Workshop Program.

9:00 - 9:15 Welcome


9:15 - 10:30 Invited Talk Volker Tresp


10:30 - 11:00 Coffee Break


11:00 - 12:30
  • Paul Groth, Antony Scerri, Ron Daniel and Bradley Allen. End-to-End Learning for Answering Structured Queries Directly over Text
  • Davide Buscaldi, Danilo Dessì, Enrico Motta, Francesco Osborne and Diego Reforgiato Recupero. Mining Scholarly Data for Fine-Grained Knowledge Graph Construction
  • Genet Asefa Gesese, Russa Biswas and Harald Sack. A Comprehensive Survey of Knowledge Graph Embeddings with Literals: Techniques and Applications

12:30 - 13:30 LUNCH BREAK



14:00 - 15:30
  • Matthias Jurisch and Bodo Igler. Graph-Convolution-Based Classification for Ontology Alignment Change Prediction
  • Pierre Monnin, Chedy Raïssi, Amedeo Napoli and Adrien Coulet. Knowledge Reconciliation with Graph Convolutional Networks: Preliminary Results
  • Sameh Mohamed, Emir Muñoz, Vit Novacek and Pierre-Yves Vandenbussche. Loss Functions in Knowledge Graph Embedding Models

15:30 - 16:00 COFFEE BREAK


16:00 - 16:45
  • Ning Pang, Weixin Zeng, Jiuyang Tang, Zhen Tan and Xiang Zhao. Iterative Entity Alignment with Improved Neural Attribute Embedding
  • Mehwish Alam. Can Knowledge Graphs and Deep Learning Approaches help in Representing, Detecting and Interpreting Metaphors?

Organizing Committee

Mehwish Alam

FIZ Karlsruhe - Leibniz Institute for Information Infrastructure, Germany

Davide Buscaldi

Labortoire d'Informatique Paris Nord (LIPN), 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

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 Årup 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.


Sponsors