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In this paper, we introduce Linked Papers With Code (LPWC), an RDF knowledgegraph that provides comprehensive, current information about almost 400,000machine learning publications. This includes the tasks addressed, the datasetsutilized, the methods implemented, and the evaluations conducted, along withtheir results. Compared to its non-RDF-based counterpart Papers With Code, LPWCnot only translates the latest advancements in machine learning into RDFformat, but also enables novel ways for scientific impact quantification andscholarly key content recommendation. LPWC is openly accessible athttps://linkedpaperswithcode.com and is licensed under CC-BY-SA 4.0. As aknowledge graph in the Linked Open Data cloud, we offer LPWC in multipleformats, from RDF dump files to a SPARQL endpoint for direct web queries, aswell as a data source with resolvable URIs and links to the data sourcesSemOpenAlex, Wikidata, and DBLP. Additionally, we supply knowledge graphembeddings, enabling LPWC to be readily applied in machine learningapplications.

Linked Papers With Code: The Latest in Machine Learning as an RDF Knowledge Graph