  In this paper, we introduce Linked Papers With Code (LPWC), an RDF knowledge
graph that provides comprehensive, current information about almost 400,000
machine learning publications. This includes the tasks addressed, the datasets
utilized, the methods implemented, and the evaluations conducted, along with
their results. Compared to its non-RDF-based counterpart Papers With Code, LPWC
not only translates the latest advancements in machine learning into RDF
format, but also enables novel ways for scientific impact quantification and
scholarly key content recommendation. LPWC is openly accessible at
https://linkedpaperswithcode.com and is licensed under CC-BY-SA 4.0. As a
knowledge graph in the Linked Open Data cloud, we offer LPWC in multiple
formats, from RDF dump files to a SPARQL endpoint for direct web queries, as
well as a data source with resolvable URIs and links to the data sources
SemOpenAlex, Wikidata, and DBLP. Additionally, we supply knowledge graph
embeddings, enabling LPWC to be readily applied in machine learning
applications.
