Workshop at ISWC 2021 on
Deep Learning for Knowledge Graphs
More Details!


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, ESWC2019, ESWC 2020 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
  • Approximate query answering on knowledge graphs
  • 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.

Keynote Speaker

Aldo Gangemi

Title: A Cognitive Stance about Knowledge Graphs and Embeddings
Abstract: Knowledge graph embeddings, and in general what kind of entity features are represented in there, are both an opportunity and a matter of concern for the cognitive scientist. We can find interesting patterns, but we also wonder whether we are getting the thing right with respect to human-centred semantics. The talk will recap some traditional issues with model-theoretic and vectorial knowledge representation, and pose some questions to KGE research.

Workshop Program and Proceedings

Workshop Program. (All times are in CEST.)

14:00 - 14:10 Opening

14:10 - 15:00 Keynote: A Cognitive Stance about Knowledge Graphs and Embeddings by Aldo Gangemi

15:00 - 15:10 Coffee break

Session 1: 15:10 - 16:10

  • 15:10 - 15:30 Quality Assessment of Knowledge Graph Hierarchies using KG-BERT Kinga Szarkowska, Veronique Moore, Pierre-Yves Vandenbussche, Paul Groth (paper)
  • 15:30 - 15:50 Language Models As or For Knowledge Bases Simon Razniewski, Andrew Yates, Nora Kassner, Gerhard Weikum (paper)
  • 15:50 - 16:10 GraphPOPE: Retaining Structural Graph Information Using Position-aware Node Embeddings Jeroen Den Boef, Joran Cornelisse, Paul Groth (paper)

16:10 - 16:30 Coffee break

Session 2: 16:30 - 17:30

  • 16:30 - 16:50 Challenges of Applying Knowledge Graph and their Embeddings to a Real-world Use-case Rick Petzold, Genet Asefa Gesese, Viktoria Bogdanova, Thorsten Zylowski, Harald Sack, Mehwish Alam (paper)
  • 16:50 - 17:10 Knowledge Graph Embeddings or Bias Graph Embeddings? A Study of Bias in Link Prediction Models Andrea Rossi, Paolo Merialdo, Donatella Firmani (paper)
  • 17:10 - 17:30 Integrating Contextual Knowledge to Visual Features for Fine Art Classification Giovanna Castellano, Giovanni Sansaro, Gennaro Vessio (paper)

17:30 - 17:45 COFFEE BREAK

Session 3: 17:45 - 18:30

  • 17:45 - 18:05 Generating Table Vector Representations Aneta Koleva, Martin Ringsquandl, Mitchell Joblin, Volker Tresp (paper)
  • 18:05 - 18:25 Understanding Class Representations: An Intrinsic Evaluation of Zero-Shot Text Classification Fabian Hoppe, Danilo Dessì, Harald Sack (paper)
  • 18:25 - 18:30 Closing

Submission Details

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

Reviewing Policy:
  • During the review stage, papers and reviews will be hidden for the public
  • The reviewers can indicate whether they want their review to remain anonymous, or not
  • Upon acceptance of a paper, the paper and the reviews become public and commenting is enabled
  • Rejected papers will remain invisible to the public (including their reviews)

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)
  • Lightning talks (1 page abstract)

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

  • Full, Short and Position paper submission deadline: Friday August 13th, 2021 Friday August 20th, 2021
  • Notification of Acceptance: Friday September 10th, 2021
  • Camera-ready paper due: Monday September 20th, 2021
  • ISWC 2021 Workshop day: Monday October 25th, 2021

Organizing Committee

Mehwish Alam

FIZ Karlsruhe - Leibniz Institute for Information Infrastructure, Germany

Davide Buscaldi

Labortoire d'Informatique Paris Nord (LIPN), Paris, France

Michael Cochez

Vrije University of Amsterdam, the Netherlands

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 (Tentative)

  • Russa Biswas, FIZ-Karlsruhe, Karlsruhe Institute of Technology, Germany.
  • Danilo Dessi', FIZ-Karlsruhe, Karlsruhe Institute of Technology, Germany.
  • Stefan Dietze, L3S Hannover, Germany.
  • Mauro Dragoni, Fondazione Bruno Kessler, Italy.
  • Genet Asefa Gesese, FIZ-Karlsruhe, Karlsruhe Institute of Technology, Germany.
  • Pascal Hitzler, Kansas State University, USA.
  • Mayank Kejriwal, Information Science Institute, University of Southern California, USA.
  • Gerard de Melo, Rutgers University, USA.
  • Amedeo Napoli, LORIA, CNRS, France.
  • Finn Årup Nielsen, Technical University of Denmark, Denmark.
  • Thiviyan Thanapalasingam, Vrije University of Amsterdam, the Netherlands.
  • Veronika Thost, IBM Research, USA.
  • Volker Tresp, Siemens AG, Germany.
  • Lei Zhang, FIZ-Karlsruhe, Karlsruhe Institute of Technology, Germany.