How Does Knowledge Evolve in Open Knowledge Graphs?

Authors Axel Polleres , Romana Pernisch , Angela Bonifati , Daniele Dell'Aglio , Daniil Dobriy , Stefania Dumbrava , Lorena Etcheverry , Nicolas Ferranti , Katja Hose , Ernesto Jiménez-Ruiz , Matteo Lissandrini , Ansgar Scherp , Riccardo Tommasini , Johannes Wachs



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Axel Polleres
  • Vienna University of Economics and Business, Austria
  • Complexity Science Hub Vienna, Austria
Romana Pernisch
  • Vrije Universiteit Amsterdam, The Netherlands
  • Discovery Lab, Elsevier, Amsterdam, The Netherlands
Angela Bonifati
  • Lyon 1 University, CNRS LIRIS, France
  • IUF, France
Daniele Dell'Aglio
  • Aalborg University, Denmark
Daniil Dobriy
  • Vienna University of Economics and Business, Austria
Stefania Dumbrava
  • ENSIIE, France
  • SAMOVAR, IP Paris, France
Lorena Etcheverry
  • Universidad de la República, Uruguay
Nicolas Ferranti
  • Vienna University of Economics and Business, Austria
Katja Hose
  • TU Wien, Austria
Ernesto Jiménez-Ruiz
  • City, University of London, UK
  • SIRIUS, University of Oslo, Norway
Matteo Lissandrini
  • Aalborg University, Denmark
Ansgar Scherp
  • Ulm University, Germany
Riccardo Tommasini
  • INSA Lyon, CNRS LIRIS, France
Johannes Wachs
  • Corvinus University of Budapest, Hungary
  • Centre for Economic and Regional Studies, Hungary
  • Complexity Science Hub Vienna, Austria

Acknowledgements

We would like to thank the reviewers for their invaluable comments that helped to improve our manuscript.

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Axel Polleres, Romana Pernisch, Angela Bonifati, Daniele Dell'Aglio, Daniil Dobriy, Stefania Dumbrava, Lorena Etcheverry, Nicolas Ferranti, Katja Hose, Ernesto Jiménez-Ruiz, Matteo Lissandrini, Ansgar Scherp, Riccardo Tommasini, and Johannes Wachs. How Does Knowledge Evolve in Open Knowledge Graphs?. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 11:1-11:59, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/TGDK.1.1.11

Abstract

Openly available, collaboratively edited Knowledge Graphs (KGs) are key platforms for the collective management of evolving knowledge. The present work aims t o provide an analysis of the obstacles related to investigating and processing specifically this central aspect of evolution in KGs. To this end, we discuss (i) the dimensions of evolution in KGs, (ii) the observability of evolution in existing, open, collaboratively constructed Knowledge Graphs over time, and (iii) possible metrics to analyse this evolution. We provide an overview of relevant state-of-the-art research, ranging from metrics developed for Knowledge Graphs specifically to potential methods from related fields such as network science. Additionally, we discuss technical approaches - and their current limitations - related to storing, analysing and processing large and evolving KGs in terms of handling typical KG downstream tasks.

Subject Classification

ACM Subject Classification
  • Information systems → Graph-based database models
  • Information systems → Data streaming
  • Information systems → Web data description languages
Keywords
  • KG evolution
  • temporal KG
  • versioned KG
  • dynamic KG

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