Details
| Original language | English |
|---|---|
| Title of host publication | Database and Expert Systems Applications - 33rd International Conference, DEXA 2022, Proceedings |
| Editors | Christine Strauss, Alfredo Cuzzocrea, Gabriele Kotsis, Ismail Khalil, A Min Tjoa |
| Publisher | Springer Science and Business Media Deutschland GmbH |
| Pages | 63-68 |
| Number of pages | 6 |
| ISBN (electronic) | 978-3-031-12423-5 |
| ISBN (print) | 9783031124228 |
| Publication status | Published - 2022 |
| Event | 33rd International Conference on Database and Expert Systems Applications, DEXA 2022 - Vienna, Austria Duration: 22 Aug 2022 → 24 Aug 2022 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 13426 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
Background: Recent years are seeing a growing impetus in the semantification of scholarly knowledge at the fine-grained level of scientific entities in knowledge graphs. The Open Research Knowledge Graph (ORKG, orkg.org) represents an important step in this direction, with thousands of scholarly contributions as structured, fine-grained, machine-readable data. There is a need, however, to engender change in traditional community practices of recording contributions as unstructured, non-machine-readable text. For this in turn, there is a strong need for AI tools designed for scientists that permit easy and accurate semantification of their scholarly contributions. We present one such tool, ORKG-assays. Implementation: ORKG-assays is a freely available AI micro-service in ORKG written in Python designed to assist scientists obtain semantified bioassays as a set of triples. It uses an AI-based clustering algorithm which on gold-standard evaluations over 900 bioassays with 5,514 unique property-value pairs for 103 predicates shows competitive performance. Results and Discussion: As a result, semantified assay collections can be surveyed on the ORKG platform via tabulation or chart-based visualizations of key property values of the chemicals and compounds offering smart knowledge access to biochemists and pharmaceutical researchers in the advancement of drug development.
Keywords
- Artificial intelligence, Bioassays, K-means clustering, Open research knowledge graph, Scholarly digital library
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- General Computer Science
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Database and Expert Systems Applications - 33rd International Conference, DEXA 2022, Proceedings. ed. / Christine Strauss; Alfredo Cuzzocrea; Gabriele Kotsis; Ismail Khalil; A Min Tjoa. Springer Science and Business Media Deutschland GmbH, 2022. p. 63-68 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13426 LNCS).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - The Digitalization of Bioassays in the Open Research Knowledge Graph
AU - D’Souza, Jennifer
AU - Monteverdi, Anita
AU - Haris, Muhammad
AU - Anteghini, Marco
AU - Farfar, Kheir Eddine
AU - Stocker, Markus
AU - dos Santos, Vitor A.P.Martins
AU - Auer, Sören
N1 - Funding Information: Keywords: Open research knowledge graph · Scholarly digital library · Bioassays · K-means clustering · Artificial intelligence Supported by TIB Leibniz Information Centre for Science and Technology, the EU H2020 ERC project ScienceGraph (GA ID: 819536) and the ITN PERICO (GA ID: 812968).
PY - 2022
Y1 - 2022
N2 - Background: Recent years are seeing a growing impetus in the semantification of scholarly knowledge at the fine-grained level of scientific entities in knowledge graphs. The Open Research Knowledge Graph (ORKG, orkg.org) represents an important step in this direction, with thousands of scholarly contributions as structured, fine-grained, machine-readable data. There is a need, however, to engender change in traditional community practices of recording contributions as unstructured, non-machine-readable text. For this in turn, there is a strong need for AI tools designed for scientists that permit easy and accurate semantification of their scholarly contributions. We present one such tool, ORKG-assays. Implementation: ORKG-assays is a freely available AI micro-service in ORKG written in Python designed to assist scientists obtain semantified bioassays as a set of triples. It uses an AI-based clustering algorithm which on gold-standard evaluations over 900 bioassays with 5,514 unique property-value pairs for 103 predicates shows competitive performance. Results and Discussion: As a result, semantified assay collections can be surveyed on the ORKG platform via tabulation or chart-based visualizations of key property values of the chemicals and compounds offering smart knowledge access to biochemists and pharmaceutical researchers in the advancement of drug development.
AB - Background: Recent years are seeing a growing impetus in the semantification of scholarly knowledge at the fine-grained level of scientific entities in knowledge graphs. The Open Research Knowledge Graph (ORKG, orkg.org) represents an important step in this direction, with thousands of scholarly contributions as structured, fine-grained, machine-readable data. There is a need, however, to engender change in traditional community practices of recording contributions as unstructured, non-machine-readable text. For this in turn, there is a strong need for AI tools designed for scientists that permit easy and accurate semantification of their scholarly contributions. We present one such tool, ORKG-assays. Implementation: ORKG-assays is a freely available AI micro-service in ORKG written in Python designed to assist scientists obtain semantified bioassays as a set of triples. It uses an AI-based clustering algorithm which on gold-standard evaluations over 900 bioassays with 5,514 unique property-value pairs for 103 predicates shows competitive performance. Results and Discussion: As a result, semantified assay collections can be surveyed on the ORKG platform via tabulation or chart-based visualizations of key property values of the chemicals and compounds offering smart knowledge access to biochemists and pharmaceutical researchers in the advancement of drug development.
KW - Artificial intelligence
KW - Bioassays
KW - K-means clustering
KW - Open research knowledge graph
KW - Scholarly digital library
UR - http://www.scopus.com/inward/record.url?scp=85135769576&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2203.14574
DO - 10.48550/arXiv.2203.14574
M3 - Conference contribution
AN - SCOPUS:85135769576
SN - 9783031124228
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 63
EP - 68
BT - Database and Expert Systems Applications - 33rd International Conference, DEXA 2022, Proceedings
A2 - Strauss, Christine
A2 - Cuzzocrea, Alfredo
A2 - Kotsis, Gabriele
A2 - Khalil, Ismail
A2 - Tjoa, A Min
PB - Springer Science and Business Media Deutschland GmbH
T2 - 33rd International Conference on Database and Expert Systems Applications, DEXA 2022
Y2 - 22 August 2022 through 24 August 2022
ER -