TY - GEN
T1 - Analyzing the Evolution of Vocabulary Terms and Their Impact on the LOD Cloud
AU - Abdel-Qader, Mohammad
AU - Scherp, Ansgar
AU - Vagliano, Iacopo
N1 - Funding Information: Acknowledgment. This work was supported by the EU’s Horizon 2020 programme under grant agreement H2020-693092 MOVING. Funding Information: This work was supported by the EU’s Horizon 2020 programme under grant agreement H2020-693092 MOVING. Publisher Copyright: © 2018, Springer International Publishing AG, part of Springer Nature.
PY - 2018
Y1 - 2018
N2 - Vocabularies are used for modeling data in Knowledge Graphs (KGs) like the Linked Open Data Cloud and Wikidata. During their lifetime, vocabularies are subject to changes. New terms are coined, while existing terms are modified or deprecated. We first quantify the amount and frequency of changes in vocabularies. Subsequently, we investigate to which extend and when the changes are adopted in the evolution of KGs. We conduct our experiments on three large-scale KGs: the Billion Triples Challenge datasets, the Dynamic Linked Data Observatory dataset, and Wikidata. Our results show that the change frequency of terms is rather low, but can have high impact due to the large amount of distributed graph data on the web. Furthermore, not all coined terms are used and most of the deprecated terms are still used by data publishers. The adoption time of terms coming from different vocabularies ranges from very fast (few days) to very slow (few years). Surprisingly, we could observe some adoptions before the vocabulary changes were published. Understanding the evolution of vocabulary terms is important to avoid wrong assumptions about the modeling status of data published on the web, which may result in difficulties when querying the data from distributed sources.
AB - Vocabularies are used for modeling data in Knowledge Graphs (KGs) like the Linked Open Data Cloud and Wikidata. During their lifetime, vocabularies are subject to changes. New terms are coined, while existing terms are modified or deprecated. We first quantify the amount and frequency of changes in vocabularies. Subsequently, we investigate to which extend and when the changes are adopted in the evolution of KGs. We conduct our experiments on three large-scale KGs: the Billion Triples Challenge datasets, the Dynamic Linked Data Observatory dataset, and Wikidata. Our results show that the change frequency of terms is rather low, but can have high impact due to the large amount of distributed graph data on the web. Furthermore, not all coined terms are used and most of the deprecated terms are still used by data publishers. The adoption time of terms coming from different vocabularies ranges from very fast (few days) to very slow (few years). Surprisingly, we could observe some adoptions before the vocabulary changes were published. Understanding the evolution of vocabulary terms is important to avoid wrong assumptions about the modeling status of data published on the web, which may result in difficulties when querying the data from distributed sources.
UR - http://www.scopus.com/inward/record.url?scp=85048515751&partnerID=8YFLogxK
U2 - https://doi.org/10.1007/978-3-319-93417-4_1
DO - https://doi.org/10.1007/978-3-319-93417-4_1
M3 - Conference contribution
SN - 9783319934167
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 1
EP - 16
BT - The Semantic Web - 15th International Conference, ESWC 2018, Proceedings
A2 - Gangemi, Aldo
A2 - Troncy, Raphaël
A2 - Navigli, Roberto
A2 - Hollink, Laura
A2 - Vidal, Maria-Esther
A2 - Hitzler, Pascal
A2 - Tordai, Anna
A2 - Alam, Mehwish
PB - Springer - Verlag
T2 - 15th International Conference on Extended Semantic Web Conference, ESWC 2018
Y2 - 3 June 2018 through 7 June 2018
ER -