@article{poll-etal-2023TGDK,
  author       = {Axel Polleres and
                  Romana Pernisch and
                  Angela Bonifati and
                  Daniele Dell'Aglio and
                  Daniil Dobriy and
                  Stefania Dumbrava and
                  Lorena Etcheverry and
                  Nicolas Ferranti and
                  Katja Hose and
                  Ernesto Jim{\'{e}}nez{-}Ruiz and
                  Matteo Lissandrini and
                  Ansgar Scherp and
                  Riccardo Tommasini and
                  Johannes Wachs},
  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.},
  title        = {How Does Knowledge Evolve in Open Knowledge Graphs?},
  journal      = {{TGDK}},
  volume       = 1,
  number       = 1,
  month        = dec,
  pages        = {11:1--11:59},
  year         = 2023,
  url          = {https://doi.org/10.4230/TGDK.1.1.11},
  doi          = {10.4230/TGDK.1.1.11},
 }

{"_id":"Ke9GaLSZGiDXALvJg","bibbaseid":"polleres-pernisch-bonifati-dellaglio-dobriy-dumbrava-etcheverry-ferranti-etal-howdoesknowledgeevolveinopenknowledgegraphs-2023","author_short":["Polleres, A.","Pernisch, R.","Bonifati, A.","Dell'Aglio, D.","Dobriy, D.","Dumbrava, S.","Etcheverry, L.","Ferranti, N.","Hose, K.","Jiménez-Ruiz, E.","Lissandrini, M.","Scherp, A.","Tommasini, R.","Wachs, J."],"bibdata":{"bibtype":"article","type":"article","author":[{"firstnames":["Axel"],"propositions":[],"lastnames":["Polleres"],"suffixes":[]},{"firstnames":["Romana"],"propositions":[],"lastnames":["Pernisch"],"suffixes":[]},{"firstnames":["Angela"],"propositions":[],"lastnames":["Bonifati"],"suffixes":[]},{"firstnames":["Daniele"],"propositions":[],"lastnames":["Dell'Aglio"],"suffixes":[]},{"firstnames":["Daniil"],"propositions":[],"lastnames":["Dobriy"],"suffixes":[]},{"firstnames":["Stefania"],"propositions":[],"lastnames":["Dumbrava"],"suffixes":[]},{"firstnames":["Lorena"],"propositions":[],"lastnames":["Etcheverry"],"suffixes":[]},{"firstnames":["Nicolas"],"propositions":[],"lastnames":["Ferranti"],"suffixes":[]},{"firstnames":["Katja"],"propositions":[],"lastnames":["Hose"],"suffixes":[]},{"firstnames":["Ernesto"],"propositions":[],"lastnames":["Jiménez-Ruiz"],"suffixes":[]},{"firstnames":["Matteo"],"propositions":[],"lastnames":["Lissandrini"],"suffixes":[]},{"firstnames":["Ansgar"],"propositions":[],"lastnames":["Scherp"],"suffixes":[]},{"firstnames":["Riccardo"],"propositions":[],"lastnames":["Tommasini"],"suffixes":[]},{"firstnames":["Johannes"],"propositions":[],"lastnames":["Wachs"],"suffixes":[]}],"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.","title":"How Does Knowledge Evolve in Open Knowledge Graphs?","journal":"TGDK","volume":"1","number":"1","month":"December","pages":"11:1–11:59","year":"2023","url":"https://doi.org/10.4230/TGDK.1.1.11","doi":"10.4230/TGDK.1.1.11","bibtex":"@article{poll-etal-2023TGDK,\n author = {Axel Polleres and\n Romana Pernisch and\n Angela Bonifati and\n Daniele Dell'Aglio and\n Daniil Dobriy and\n Stefania Dumbrava and\n Lorena Etcheverry and\n Nicolas Ferranti and\n Katja Hose and\n Ernesto Jim{\\'{e}}nez{-}Ruiz and\n Matteo Lissandrini and\n Ansgar Scherp and\n Riccardo Tommasini and\n Johannes Wachs},\n 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.},\n title = {How Does Knowledge Evolve in Open Knowledge Graphs?},\n journal = {{TGDK}},\n volume = 1,\n number = 1,\n month = dec,\n pages = {11:1--11:59},\n year = 2023,\n url = {https://doi.org/10.4230/TGDK.1.1.11},\n doi = {10.4230/TGDK.1.1.11},\n }\n\n\n","author_short":["Polleres, A.","Pernisch, R.","Bonifati, A.","Dell'Aglio, D.","Dobriy, D.","Dumbrava, S.","Etcheverry, L.","Ferranti, N.","Hose, K.","Jiménez-Ruiz, E.","Lissandrini, M.","Scherp, A.","Tommasini, R.","Wachs, J."],"key":"poll-etal-2023TGDK","id":"poll-etal-2023TGDK","bibbaseid":"polleres-pernisch-bonifati-dellaglio-dobriy-dumbrava-etcheverry-ferranti-etal-howdoesknowledgeevolveinopenknowledgegraphs-2023","role":"author","urls":{"Paper":"https://doi.org/10.4230/TGDK.1.1.11"},"metadata":{"authorlinks":{}},"downloads":1},"bibtype":"article","biburl":"www.polleres.net/mypublications.bib","dataSources":["qyx6bB8ujfH9zqTzu","gixxkiKt6rtWGoKSh","cBfwyqsLFQQMc4Fss"],"keywords":[],"search_terms":["knowledge","evolve","open","knowledge","graphs","polleres","pernisch","bonifati","dell'aglio","dobriy","dumbrava","etcheverry","ferranti","hose","jiménez-ruiz","lissandrini","scherp","tommasini","wachs"],"title":"How Does Knowledge Evolve in Open Knowledge Graphs?","year":2023,"downloads":1}