@article{polleres_how_2023,
	title = {How {Does} {Knowledge} {Evolve} in {Open} {Knowledge} {Graphs}?},
	copyright = {https://creativecommons.org/licenses/by/4.0/legalcode},
	url = {https://drops.dagstuhl.de/entities/document/10.4230/TGDK.1.1.11},
	doi = {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.},
	language = {en},
	urldate = {2023-12-20},
	journal = {DROPS-IDN/v2/document/10.4230/TGDK.1.1.11},
	author = {Polleres, Axel and Pernisch, Romana and Bonifati, Angela and Dell'Aglio, Daniele and Dobriy, Daniil and Dumbrava, Stefania and Etcheverry, Lorena and Ferranti, Nicolas and Hose, Katja and Jiménez-Ruiz, Ernesto and Lissandrini, Matteo and Scherp, Ansgar and Tommasini, Riccardo and Wachs, Johannes},
	year = {2023},
}

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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.","language":"en","urldate":"2023-12-20","journal":"DROPS-IDN/v2/document/10.4230/TGDK.1.1.11","author":[{"propositions":[],"lastnames":["Polleres"],"firstnames":["Axel"],"suffixes":[]},{"propositions":[],"lastnames":["Pernisch"],"firstnames":["Romana"],"suffixes":[]},{"propositions":[],"lastnames":["Bonifati"],"firstnames":["Angela"],"suffixes":[]},{"propositions":[],"lastnames":["Dell'Aglio"],"firstnames":["Daniele"],"suffixes":[]},{"propositions":[],"lastnames":["Dobriy"],"firstnames":["Daniil"],"suffixes":[]},{"propositions":[],"lastnames":["Dumbrava"],"firstnames":["Stefania"],"suffixes":[]},{"propositions":[],"lastnames":["Etcheverry"],"firstnames":["Lorena"],"suffixes":[]},{"propositions":[],"lastnames":["Ferranti"],"firstnames":["Nicolas"],"suffixes":[]},{"propositions":[],"lastnames":["Hose"],"firstnames":["Katja"],"suffixes":[]},{"propositions":[],"lastnames":["Jiménez-Ruiz"],"firstnames":["Ernesto"],"suffixes":[]},{"propositions":[],"lastnames":["Lissandrini"],"firstnames":["Matteo"],"suffixes":[]},{"propositions":[],"lastnames":["Scherp"],"firstnames":["Ansgar"],"suffixes":[]},{"propositions":[],"lastnames":["Tommasini"],"firstnames":["Riccardo"],"suffixes":[]},{"propositions":[],"lastnames":["Wachs"],"firstnames":["Johannes"],"suffixes":[]}],"year":"2023","bibtex":"@article{polleres_how_2023,\n\ttitle = {How {Does} {Knowledge} {Evolve} in {Open} {Knowledge} {Graphs}?},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/legalcode},\n\turl = {https://drops.dagstuhl.de/entities/document/10.4230/TGDK.1.1.11},\n\tdoi = {10.4230/TGDK.1.1.11},\n\tabstract = {Openly available, collaboratively edited Knowledge Graphs (KGs) are key platforms for the collective management of evolving knowledge. 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