DL4KG 2021
Deep Learning for Knowledge Graphs 2021

Table of Contents

• Preface
  Summary: There were 10 papers submitted for peer-review to this workshop. Out of these, 8 papers were accepted for this volume.

Papers

• Quality Assessment of Knowledge Graph Hierarchies using KG-BERT
  Kinga Szarkowska, Veronique Moore, Pierre-Yves Vandenbussche, Paul Groth
• Language Models As or For Knowledge Bases
  Simon Razniewski, Andrew Yates, Nora Kassner, Gerhard Weikum
• GraphPOPE: Retaining Structural Graph Information Using Position-aware Node Embeddings
  Jeroen Den Boef, Joran Cornelisse, Paul Groth
• 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
• Knowledge Graph Embeddings or Bias Graph Embeddings? A Study of Bias in Link Prediction Models
  Andrea Rossi, Paolo Merialdo, Donatella Firmani
• Integrating Contextual Knowledge to Visual Features for Fine Art Classification
  Giovanna Castellano, Giovanni Sansaro, Gennaro Vessio
• Generating Table Vector Representations
  Aneta Koleva, Martin Ringsquandl, Mitchell Joblin, Volker Tresp
• Understanding Class Representations: An Intrinsic Evaluation of Zero-Shot Text Classification
  Fabian Hoppe, Danilo Dessì, Harald Sack