Details
| Original language | English |
|---|---|
| Title of host publication | From Born-Physical to Born-Virtual |
| Subtitle of host publication | Augmenting Intelligence in Digital Libraries - 24th International Conference on Asian Digital Libraries, ICADL 2022, Proceedings |
| Editors | Yuen-Hsien Tseng, Marie Katsurai, Hoa N. Nguyen |
| Publisher | Springer Science and Business Media Deutschland GmbH |
| Pages | 253-269 |
| Number of pages | 17 |
| ISBN (print) | 9783031217555 |
| Publication status | Published - 7 Dec 2022 |
| Event | 24th International Conference on Asia-Pacific Digital Libraries, ICADL 2022 - Hanoi, Viet Nam Duration: 30 Nov 2022 → 2 Dec 2022 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 13636 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
Knowledge Graphs (KG) have gained increasing importance in science, business and society in the last years. However, most knowledge graphs were either extracted or compiled from existing sources. There are only relatively few examples where knowledge graphs were genuinely created by an intertwined human-machine collaboration. Also, since the quality of data and knowledge graphs is of paramount importance, a number of data quality assessment models have been proposed. However, they do not take the specific aspects of intertwined human-machine curated knowledge graphs into account. In this work, we propose a graded maturity model for scholarly knowledge graphs (KGMM), which specifically focuses on aspects related to the joint, evolutionary curation of knowledge graphs for digital libraries. Our model comprises 5 maturity stages with 20 quality measures. We demonstrate the implementation of our model in a large scale scholarly knowledge graph curation effort.
Keywords
- Human-machine collaboration, Knowledge graph, Linked Open Data (LOD), Maturity model
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- General Computer Science
Cite this
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From Born-Physical to Born-Virtual: Augmenting Intelligence in Digital Libraries - 24th International Conference on Asian Digital Libraries, ICADL 2022, Proceedings. ed. / Yuen-Hsien Tseng; Marie Katsurai; Hoa N. Nguyen. Springer Science and Business Media Deutschland GmbH, 2022. p. 253-269 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13636 LNCS).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - KGMM
T2 - 24th International Conference on Asia-Pacific Digital Libraries, ICADL 2022
AU - Hussein, Hassan
AU - Oelen, Allard
AU - Karras, Oliver
AU - Auer, Sören
PY - 2022/12/7
Y1 - 2022/12/7
N2 - Knowledge Graphs (KG) have gained increasing importance in science, business and society in the last years. However, most knowledge graphs were either extracted or compiled from existing sources. There are only relatively few examples where knowledge graphs were genuinely created by an intertwined human-machine collaboration. Also, since the quality of data and knowledge graphs is of paramount importance, a number of data quality assessment models have been proposed. However, they do not take the specific aspects of intertwined human-machine curated knowledge graphs into account. In this work, we propose a graded maturity model for scholarly knowledge graphs (KGMM), which specifically focuses on aspects related to the joint, evolutionary curation of knowledge graphs for digital libraries. Our model comprises 5 maturity stages with 20 quality measures. We demonstrate the implementation of our model in a large scale scholarly knowledge graph curation effort.
AB - Knowledge Graphs (KG) have gained increasing importance in science, business and society in the last years. However, most knowledge graphs were either extracted or compiled from existing sources. There are only relatively few examples where knowledge graphs were genuinely created by an intertwined human-machine collaboration. Also, since the quality of data and knowledge graphs is of paramount importance, a number of data quality assessment models have been proposed. However, they do not take the specific aspects of intertwined human-machine curated knowledge graphs into account. In this work, we propose a graded maturity model for scholarly knowledge graphs (KGMM), which specifically focuses on aspects related to the joint, evolutionary curation of knowledge graphs for digital libraries. Our model comprises 5 maturity stages with 20 quality measures. We demonstrate the implementation of our model in a large scale scholarly knowledge graph curation effort.
KW - Human-machine collaboration
KW - Knowledge graph
KW - Linked Open Data (LOD)
KW - Maturity model
UR - http://www.scopus.com/inward/record.url?scp=85145008200&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2211.12223
DO - 10.48550/arXiv.2211.12223
M3 - Conference contribution
AN - SCOPUS:85145008200
SN - 9783031217555
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 253
EP - 269
BT - From Born-Physical to Born-Virtual
A2 - Tseng, Yuen-Hsien
A2 - Katsurai, Marie
A2 - Nguyen, Hoa N.
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 30 November 2022 through 2 December 2022
ER -