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Knowledge Graph Embeddings: Open Challenges and Opportunities

Authors Russa Biswas , Lucie-Aimée Kaffee , Michael Cochez , Stefania Dumbrava , Theis E. Jendal , Matteo Lissandrini , Vanessa Lopez , Eneldo Loza Mencía , Heiko Paulheim , Harald Sack , Edlira Kalemi Vakaj , Gerard de Melo



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Author Details

Russa Biswas
  • Hasso-Plattner Institut, Potsdam, Germany
Lucie-Aimée Kaffee
  • Hasso-Plattner-Institut, Potsdam, Germany
Michael Cochez
  • Vrije Universiteit Amsterdam, The Netherlands
  • Elsevier Discovery Lab, Netherlands
Stefania Dumbrava
  • ENSIIE, France
Theis E. Jendal
  • Aalborg University, Denmark
Matteo Lissandrini
  • Aalborg University, Denmark
Vanessa Lopez
  • IBM Research Dublin, Ireland
Eneldo Loza Mencía
  • research.eneldo.net, Frankfurt, Germany
Heiko Paulheim
  • Universität Mannheim, Germany
Harald Sack
  • FIZ Karlsruhe, Germany
  • Karlsruhe Institute of Technology, AIFB, Germany
Edlira Kalemi Vakaj
  • Birmingham City University, UK
Gerard de Melo
  • Hasso-Plattner Institut, Potsdam, Germany
  • University of Potsdam, Germany

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Russa Biswas, Lucie-Aimée Kaffee, Michael Cochez, Stefania Dumbrava, Theis E. Jendal, Matteo Lissandrini, Vanessa Lopez, Eneldo Loza Mencía, Heiko Paulheim, Harald Sack, Edlira Kalemi Vakaj, and Gerard de Melo. Knowledge Graph Embeddings: Open Challenges and Opportunities. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 4:1-4:32, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/TGDK.1.1.4

Abstract

While Knowledge Graphs (KGs) have long been used as valuable sources of structured knowledge, in recent years, KG embeddings have become a popular way of deriving numeric vector representations from them, for instance, to support knowledge graph completion and similarity search. This study surveys advances as well as open challenges and opportunities in this area. For instance, the most prominent embedding models focus primarily on structural information. However, there has been notable progress in incorporating further aspects, such as semantics, multi-modal, temporal, and multilingual features. Most embedding techniques are assessed using human-curated benchmark datasets for the task of link prediction, neglecting other important real-world KG applications. Many approaches assume a static knowledge graph and are unable to account for dynamic changes. Additionally, KG embeddings may encode data biases and lack interpretability. Overall, this study provides an overview of promising research avenues to learn improved KG embeddings that can address a more diverse range of use cases.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Machine learning approaches
  • Computing methodologies → Semantic networks
Keywords
  • Knowledge Graphs
  • KG embeddings
  • Link prediction
  • KG applications

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