TY - GEN

T1 - Scholarly Knowledge Extraction from Published Software Packages

AU - Haris, Muhammad

AU - Stocker, Markus

AU - Auer, Sören

N1 - Funding Information: Acknowledgment. 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 - 2022/12/7

Y1 - 2022/12/7

N2 - A plethora of scientific software packages are published in repositories, e.g., Zenodo and figshare. These software packages are crucial for the reproducibility of published research. As an additional route to scholarly knowledge graph construction, we propose an approach for automated extraction of machine actionable (structured) scholarly knowledge from published software packages by static analysis of their (meta)data and contents (in particular scripts in languages such as Python). The approach can be summarized as follows. First, we extract metadata information (software description, programming languages, related references) from software packages by leveraging the Software Metadata Extraction Framework (SOMEF) and the GitHub API. Second, we analyze the extracted metadata to find the research articles associated with the corresponding software repository. Third, for software contained in published packages, we create and analyze the Abstract Syntax Tree (AST) representation to extract information about the procedures performed on data. Fourth, we search the extracted information in the full text of related articles to constrain the extracted information to scholarly knowledge, i.e. information published in the scholarly literature. Finally, we publish the extracted machine actionable scholarly knowledge in the Open Research Knowledge Graph (ORKG).

AB - A plethora of scientific software packages are published in repositories, e.g., Zenodo and figshare. These software packages are crucial for the reproducibility of published research. As an additional route to scholarly knowledge graph construction, we propose an approach for automated extraction of machine actionable (structured) scholarly knowledge from published software packages by static analysis of their (meta)data and contents (in particular scripts in languages such as Python). The approach can be summarized as follows. First, we extract metadata information (software description, programming languages, related references) from software packages by leveraging the Software Metadata Extraction Framework (SOMEF) and the GitHub API. Second, we analyze the extracted metadata to find the research articles associated with the corresponding software repository. Third, for software contained in published packages, we create and analyze the Abstract Syntax Tree (AST) representation to extract information about the procedures performed on data. Fourth, we search the extracted information in the full text of related articles to constrain the extracted information to scholarly knowledge, i.e. information published in the scholarly literature. Finally, we publish the extracted machine actionable scholarly knowledge in the Open Research Knowledge Graph (ORKG).

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=85145010085&partnerID=8YFLogxK

U2 - https://doi.org/10.48550/arXiv.2212.07921

DO - https://doi.org/10.48550/arXiv.2212.07921

M3 - Conference contribution

AN - SCOPUS:85145010085

SN - 9783031217555

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 301

EP - 310

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 -