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How Does Knowledge Evolve in Open Knowledge Graphs?

Authors :
Polleres, Axel
Pernisch, Romana
Bonifati, Angela
Dell'Aglio, Daniele
Dobriy, Daniil
Dumbrava, Stefania
Etcheverry, Lorena
Ferranti, Nicolas
Hose, Katja
Jiménez-Ruiz, Ernesto
Lissandrini, Matteo
Scherp, Ansgar
Tommasini, Riccardo
Wachs, Johannes
Source :
Transactions on Graph Data and Knowledge, Vol 1, Iss 1, Pp 11:1-11:59 (2023)
Publication Year :
2023
Publisher :
Schloss Dagstuhl -- Leibniz-Zentrum fuer Informatik, 2023.

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.

Details

Language :
English
ISSN :
29427517
Volume :
1
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Transactions on Graph Data and Knowledge
Publication Type :
Academic Journal
Accession number :
edsdoj.41255811dc4468882afba6d1e04163e
Document Type :
article
Full Text :
https://doi.org/10.4230/TGDK.1.1.11