From Code Archival to Knowledge Graph: Bridging Software Heritage, COAR Notify and Wikidata

Abstract:Software is a first-class scientific object, yet validated links between source code and the scholarly record remain largely absent from the Linked Open Data (LOD) cloud, isolating archived artefacts from semantic discovery. This paper presents an end-to-end reconciliation pipeline that harvests, validates, and models publication-to-repository pairs from sources where the link between a paper and its source code is explicit and editorially verified: the software-centric journals JOSS, SoftwareX, and IPOL, together with the reproducibility reports of the SIGMOD Availability and Reproducibility Initiative (ARI). This yields a curated corpus of 4,397 $\langle$DOI, repository-URL$\rangle$ pairs. We design two distinct application profiles grounded in Wikidata classes (one for scholarly articles, one for software instances) aligned with the this http URL and CodeMeta vocabularies. This architectural separation enables rule-based reconciliation at two granularities: lightweight, inline publication references or standalone, first-class Wikidata software nodes equipped with SWHIDs, Software Heritage's content-addressed identifiers. A read-only lookup against Wikidata shows that only 82 of the harvested repositories were already modelled there; human-reviewed batches have since created 4{,}182 new software items cross-linked to their articles. We further show that payloads of the emerging COAR Notify protocol, an external effort we do not develop, map natively onto our input format, so the same backend could later serve a live enrichment stream. Our core contribution is a pair of application profiles that turn Wikidata into a connector between the scholarly record and archived source code; we openly release all code, application profiles, and harvested datasets.

15 pages, 4 figures. Accepted at the 7th Wikidata Workshop (Wikidata 2026), co-located with ISWC 2026. Open-source pipeline and code available at this https URL

Digital Libraries (cs.DL); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)

H.3.5; H.3.7; H.2.8

arXiv:2609.21667 [cs.DL]

(or arXiv:2609.21667v1 [cs.DL] for this version)

https://doi.org/10.48550/arXiv.2609.21667 arXiv-issued DOI via DataCite (pending registration)