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 | 170-179 |
| 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
The value of structured scholarly knowledge for research and society at large is well understood, but producing scholarly knowledge (i.e., knowledge traditionally published in articles) in structured form remains a challenge. We propose an approach for automatically extracting scholarly knowledge from published software packages by static analysis of their metadata and contents (scripts and data) and populating a scholarly knowledge graph with the extracted knowledge. Our approach is based on mining scientific software packages linked to article publications by extracting metadata and analyzing the Abstract Syntax Tree (AST) of the source code to obtain information about the used and produced data as well as operations performed on data. The resulting knowledge graph includes articles, software packages metadata, and computational techniques applied to input data utilized as materials in research work. The knowledge graph also includes the results reported as scholarly knowledge in articles. Our code is available on GitHub at the following link: https://github.com/mharis111/parse-software-scripts.
Keywords
- Abstract Syntax Tree, Analyzing Software Packages, Code Analysis, Machine Actionability, Open Research Knowledge Graph, Scholarly Communication
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. 170-179 (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 - Scholarly Knowledge Graph Construction from Published Software Packages
AU - Haris, Muhammad
AU - Auer, Sören
AU - Stocker, Markus
N1 - Funding Information: This work was co-funded by the European Research Council for the project ScienceGRAPH (Grant agreement ID: 819536) and TIB–Leibniz Information Centre for Science and Technology.
PY - 2023/11/30
Y1 - 2023/11/30
N2 - The value of structured scholarly knowledge for research and society at large is well understood, but producing scholarly knowledge (i.e., knowledge traditionally published in articles) in structured form remains a challenge. We propose an approach for automatically extracting scholarly knowledge from published software packages by static analysis of their metadata and contents (scripts and data) and populating a scholarly knowledge graph with the extracted knowledge. Our approach is based on mining scientific software packages linked to article publications by extracting metadata and analyzing the Abstract Syntax Tree (AST) of the source code to obtain information about the used and produced data as well as operations performed on data. The resulting knowledge graph includes articles, software packages metadata, and computational techniques applied to input data utilized as materials in research work. The knowledge graph also includes the results reported as scholarly knowledge in articles. Our code is available on GitHub at the following link: https://github.com/mharis111/parse-software-scripts.
AB - The value of structured scholarly knowledge for research and society at large is well understood, but producing scholarly knowledge (i.e., knowledge traditionally published in articles) in structured form remains a challenge. We propose an approach for automatically extracting scholarly knowledge from published software packages by static analysis of their metadata and contents (scripts and data) and populating a scholarly knowledge graph with the extracted knowledge. Our approach is based on mining scientific software packages linked to article publications by extracting metadata and analyzing the Abstract Syntax Tree (AST) of the source code to obtain information about the used and produced data as well as operations performed on data. The resulting knowledge graph includes articles, software packages metadata, and computational techniques applied to input data utilized as materials in research work. The knowledge graph also includes the results reported as scholarly knowledge in articles. Our code is available on GitHub at the following link: https://github.com/mharis111/parse-software-scripts.
KW - Abstract Syntax Tree
KW - Analyzing Software Packages
KW - Code Analysis
KW - Machine Actionability
KW - Open Research Knowledge Graph
KW - Scholarly Communication
UR - http://www.scopus.com/inward/record.url?scp=85180152166&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2312.01065
DO - 10.48550/arXiv.2312.01065
M3 - Conference contribution
AN - SCOPUS:85180152166
SN - 9789819980871
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 170
EP - 179
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 -