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 | 477-484 |
| Number of pages | 8 |
| ISBN (electronic) | 978-3-031-21756-2 |
| ISBN (print) | 9783031217555 |
| Publication status | Published - 2022 |
| Externally published | Yes |
| 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
When semantically describing knowledge graphs (KGs), users have to make a critical choice of a vocabulary (i.e. predicates and resources). The success of KG building is determined by the convergence of shared vocabularies so that meaning can be established. The typical lifecycle for a new KG construction can be defined as follows: nascent phases of graph construction experience terminology divergence, while later phases of graph construction experience terminology convergence and reuse. In this paper, we describe our approach tailoring two AI-based clustering algorithms for recommending predicates (in RDF statements) about resources in the Open Research Knowledge Graph (ORKG) https://orkg.org/. Such a service to recommend existing predicates to semantify new incoming data of scholarly publications is of paramount importance for fostering terminology convergence in the ORKG. Our experiments show very promising results: a high precision with relatively high recall in linear runtime performance. Furthermore, this work offers novel insights into the predicate groups that automatically accrue loosely as generic semantification patterns for semantification of scholarly knowledge spanning 44 research fields.
Keywords
- Artificial intelligence, Clustering algorithms, Content-based recommender systems, Open research knowledge graph
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. 477-484 (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 - Clustering Semantic Predicates in the Open Research Knowledge Graph
AU - Arab Oghli, Omar
AU - D’Souza, Jennifer
AU - Auer, Sören
N1 - Funding Information: Supported by TIB Leibniz Information Centre for Science and Technology, the EU H2020 ERC project ScienceGRaph (GA ID: 819536).
PY - 2022
Y1 - 2022
N2 - When semantically describing knowledge graphs (KGs), users have to make a critical choice of a vocabulary (i.e. predicates and resources). The success of KG building is determined by the convergence of shared vocabularies so that meaning can be established. The typical lifecycle for a new KG construction can be defined as follows: nascent phases of graph construction experience terminology divergence, while later phases of graph construction experience terminology convergence and reuse. In this paper, we describe our approach tailoring two AI-based clustering algorithms for recommending predicates (in RDF statements) about resources in the Open Research Knowledge Graph (ORKG) https://orkg.org/. Such a service to recommend existing predicates to semantify new incoming data of scholarly publications is of paramount importance for fostering terminology convergence in the ORKG. Our experiments show very promising results: a high precision with relatively high recall in linear runtime performance. Furthermore, this work offers novel insights into the predicate groups that automatically accrue loosely as generic semantification patterns for semantification of scholarly knowledge spanning 44 research fields.
AB - When semantically describing knowledge graphs (KGs), users have to make a critical choice of a vocabulary (i.e. predicates and resources). The success of KG building is determined by the convergence of shared vocabularies so that meaning can be established. The typical lifecycle for a new KG construction can be defined as follows: nascent phases of graph construction experience terminology divergence, while later phases of graph construction experience terminology convergence and reuse. In this paper, we describe our approach tailoring two AI-based clustering algorithms for recommending predicates (in RDF statements) about resources in the Open Research Knowledge Graph (ORKG) https://orkg.org/. Such a service to recommend existing predicates to semantify new incoming data of scholarly publications is of paramount importance for fostering terminology convergence in the ORKG. Our experiments show very promising results: a high precision with relatively high recall in linear runtime performance. Furthermore, this work offers novel insights into the predicate groups that automatically accrue loosely as generic semantification patterns for semantification of scholarly knowledge spanning 44 research fields.
KW - Artificial intelligence
KW - Clustering algorithms
KW - Content-based recommender systems
KW - Open research knowledge graph
UR - http://www.scopus.com/inward/record.url?scp=85145007977&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2210.02034
DO - 10.48550/arXiv.2210.02034
M3 - Conference contribution
AN - SCOPUS:85145007977
SN - 9783031217555
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 477
EP - 484
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
T2 - 24th International Conference on Asia-Pacific Digital Libraries, ICADL 2022
Y2 - 30 November 2022 through 2 December 2022
ER -