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

TL;DR

This paper presents Linked Papers With Code (LPWC), an RDF knowledge graph that systematically organizes and links machine learning publications, datasets, methods, and results to facilitate advanced querying, impact analysis, and integration with other scholarly data sources.

Contribution

The paper introduces LPWC, a comprehensive RDF knowledge graph for machine learning literature, enabling new scientific impact metrics and content recommendation methods.

Findings

LPWC covers nearly 400,000 publications.

Provides multiple access formats including RDF dump and SPARQL endpoint.

Includes knowledge graph embeddings for machine learning applications.

Abstract

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.