Details
| Original language | English |
|---|---|
| Title of host publication | Leveraging Generative Intelligence in Digital Libraries |
| Subtitle of host publication | Towards Human-Machine Collaboration |
| Editors | Dion H. Goh, Shu-Jiun Chen, Suppawong Tuarob |
| Publisher | Springer Science and Business Media Deutschland GmbH |
| Pages | 220-229 |
| Number of pages | 10 |
| ISBN (electronic) | 978-981-99-8088-8 |
| ISBN (print) | 9789819980871 |
| Publication status | Published - 30 Nov 2023 |
| Event | 25th International Conference on Asia-Pacific Digital Libraries, ICADL 2023 - Taipei, Taiwan Duration: 4 Dec 2023 → 7 Dec 2023 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 14458 LNNS |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
One of the pillars of the scientific method is reproducibility – the ability to replicate the results of a prior study if the same procedures are followed. A lack of reproducibility can lead to wasted resources, false conclusions, and a loss of public trust in science. Ensuring reproducibility is challenging due to the heterogeneity of the methods used in different fields of science. In this article, we present an approach for increasing the reproducibility of research results, by semantically describing and interlinking relevant artifacts such as data, software scripts or simulations in a knowledge graph. In order to ensure the flexibility to adapt the approach to different fields of science, we devise a template model, which allows defining typical descriptions required to increase reproducibility of a certain type of study. We provide a scoring model for gradually assessing the reproducibility of a certain study based on the templates and provide a knowledge graph infrastructure for curating reproducibility descriptions along with semantic research contribution descriptions. We demonstrate the feasibility of our approach with an example in data science.
Keywords
- FAIR Data Principles, Reproducibility Assessment, Scholarly Knowledge Graph
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
Cite this
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Leveraging Generative Intelligence in Digital Libraries: Towards Human-Machine Collaboration . ed. / Dion H. Goh; Shu-Jiun Chen; Suppawong Tuarob. Springer Science and Business Media Deutschland GmbH, 2023. p. 220-229 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14458 LNNS).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Increasing Reproducibility in Science by Interlinking Semantic Artifact Descriptions in a Knowledge Graph
AU - Hussein, Hassan
AU - Farfar, Kheir Eddine
AU - Oelen, Allard
AU - Karras, Oliver
AU - Auer, Sören
PY - 2023/11/30
Y1 - 2023/11/30
N2 - One of the pillars of the scientific method is reproducibility – the ability to replicate the results of a prior study if the same procedures are followed. A lack of reproducibility can lead to wasted resources, false conclusions, and a loss of public trust in science. Ensuring reproducibility is challenging due to the heterogeneity of the methods used in different fields of science. In this article, we present an approach for increasing the reproducibility of research results, by semantically describing and interlinking relevant artifacts such as data, software scripts or simulations in a knowledge graph. In order to ensure the flexibility to adapt the approach to different fields of science, we devise a template model, which allows defining typical descriptions required to increase reproducibility of a certain type of study. We provide a scoring model for gradually assessing the reproducibility of a certain study based on the templates and provide a knowledge graph infrastructure for curating reproducibility descriptions along with semantic research contribution descriptions. We demonstrate the feasibility of our approach with an example in data science.
AB - One of the pillars of the scientific method is reproducibility – the ability to replicate the results of a prior study if the same procedures are followed. A lack of reproducibility can lead to wasted resources, false conclusions, and a loss of public trust in science. Ensuring reproducibility is challenging due to the heterogeneity of the methods used in different fields of science. In this article, we present an approach for increasing the reproducibility of research results, by semantically describing and interlinking relevant artifacts such as data, software scripts or simulations in a knowledge graph. In order to ensure the flexibility to adapt the approach to different fields of science, we devise a template model, which allows defining typical descriptions required to increase reproducibility of a certain type of study. We provide a scoring model for gradually assessing the reproducibility of a certain study based on the templates and provide a knowledge graph infrastructure for curating reproducibility descriptions along with semantic research contribution descriptions. We demonstrate the feasibility of our approach with an example in data science.
KW - FAIR Data Principles
KW - Reproducibility Assessment
KW - Scholarly Knowledge Graph
UR - http://www.scopus.com/inward/record.url?scp=85180149614&partnerID=8YFLogxK
U2 - 10.15488/16377
DO - 10.15488/16377
M3 - Conference contribution
AN - SCOPUS:85180149614
SN - 9789819980871
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 220
EP - 229
BT - Leveraging Generative Intelligence in Digital Libraries
A2 - Goh, Dion H.
A2 - Chen, Shu-Jiun
A2 - Tuarob, Suppawong
PB - Springer Science and Business Media Deutschland GmbH
T2 - 25th International Conference on Asia-Pacific Digital Libraries, ICADL 2023
Y2 - 4 December 2023 through 7 December 2023
ER -