# SoMeSci- A 5 Star Open Data Gold Standard Knowledge Graph of Software Mentions in Scientific Articles

arXiv: https://arxiv.org/abs/2108.09070 · HTML: https://mlsanity.com/paper/2108.09070/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 36 references that are in the map.
- First posted on arXiv 20 Aug 2021.

## Builds on (most cited first, 5 of 5)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Investigating Software Usage in the Social Sciences: A Knowledge Graph Approach](https://mlsanity.com/paper/2003.10715/) (2020)
- [Understanding Software in Research: Initial Results from Examining Nature and a Call for Collaboration](https://mlsanity.com/paper/1706.06527/) (2017)
- [Schroedinger's code: A preliminary study on research source code availability and link persistence in astrophysics](https://mlsanity.com/paper/1801.02094/) (2018)

## Built on by (most cited first, 11 of 11)

- [UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition](https://mlsanity.com/paper/2308.03279/) (2023)
- [Into the Single Cell Multiverse: an End-to-End Dataset for Procedural Knowledge Extraction in Biomedical Texts](https://mlsanity.com/paper/2309.01812/) (2023)
- [Enhancing Software-Related Information Extraction via Single-Choice Question Answering with Large Language Models](https://mlsanity.com/paper/2404.05587/) (2024)
- [Software Mention Recognition with a Three-Stage Framework Based on BERTology Models at SOMD 2024](https://mlsanity.com/paper/2405.01575/) (2024)
- [Falcon 7b for Software Mention Detection in Scholarly Documents](https://mlsanity.com/paper/2405.08514/) (2024)
- [NFDI4DS Shared Tasks for Scholarly Document Processing](https://mlsanity.com/paper/2509.22141/) (2025)
- [NERdME: a Named Entity Recognition Dataset for Indexing Research Artifacts in Code Repositories](https://mlsanity.com/paper/2603.05750/) (2026)
- [Supporting Workflow Reproducibility by Linking Bioinformatics Tools across Papers and Executable Code](https://mlsanity.com/paper/2603.08195/) (2026)
- [Semantic Centroids and Hierarchical Density-Based Clustering for Cross-Document Software Coreference Resolution](https://mlsanity.com/paper/2603.24246/) (2026)
- [Do Lexical and Contextual Coreference Resolution Systems Degrade Differently under Mention Noise? An Empirical Study on Scientific Software Mentions](https://mlsanity.com/paper/2604.02171/) (2026)
- [From Code Archival to Knowledge Graph: Bridging Software Heritage, COAR Notify and Wikidata](https://mlsanity.com/paper/2609.21667/) (2026)

## Abstract (arXiv)

Knowledge about software used in scientific investigations is important for several reasons, for instance, to enable an understanding of provenance and methods involved in data handling. However, software is usually not formally cited, but rather mentioned informally within the scholarly description of the investigation, raising the need for automatic information extraction and disambiguation. Given the lack of reliable ground truth data, we present SoMeSci (Software Mentions in Science) a gold standard knowledge graph of software mentions in scientific articles. It contains high quality annotations (IRR: kappa=.82) of 3756 software mentions in 1367 PubMed Central articles. Besides the plain mention of the software, we also provide relation labels for additional information, such as the version, the developer, a URL or citations. Moreover, we distinguish between different types, such as application, plugin or programming environment, as well as different types of mentions, such as usage or creation. To the best of our knowledge, SoMeSci is the most comprehensive corpus about software mentions in scientific articles, providing training samples for Named Entity Recognition, Relation Extraction, Entity Disambiguation, and Entity Linking. Finally, we sketch potential use cases and provide baseline results.