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

Färber, Michael ORCID iD icon 1; Lamprecht, David 1
1 Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB), Karlsruher Institut für Technologie (KIT)

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 this https URL 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.


Volltext §
DOI: 10.5445/IR/1000168915
Veröffentlicht am 28.02.2024
Originalveröffentlichung
DOI: 10.48550/arXiv.2310.20475
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Zitationen: 1
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)
Publikationstyp Forschungsbericht/Preprint
Publikationsjahr 2023
Sprache Englisch
Identifikator KITopen-ID: 1000168915
Umfang 5 S.
Vorab online veröffentlicht am 31.10.2023
Schlagwörter Scholarly Data, Open Science, Ontology Engineering, Machine Learning
Nachgewiesen in arXiv
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