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https://linkedpaperswithcode.com/ (LPWC) is an RDFization of https://paperswithcode.com/ by @davidlamprecht and friends.
The source is at https://github.com/metaphacts/linkedpaperswithcode.
It has 10,648,824 triples, 486k publications, 205k github repos, 69k evaluations, 11k datasets, 5k tasks, 2.2k methods. https://paperswithcode.com/ is quite smaller than arxiv (imho the preeminent resource of ML innovations), but is 99% ML/AI and has a lot more detail than arxiv.
A major feature of LPWC is that it's connected to SemOpenAlex, i.e. authors and institutions are disambiguated and connected to LOD.
It's stored in GraphDB 10 and connected by internal federation to SemOpenAlex
https://linkedpaperswithcode.com/ (LPWC) is an RDFization of https://paperswithcode.com/ by @davidlamprecht and friends.
The source is at https://github.com/metaphacts/linkedpaperswithcode.
It has 10,648,824 triples, 486k publications, 205k github repos, 69k evaluations, 11k datasets, 5k tasks, 2.2k methods.
https://paperswithcode.com/ is quite smaller than arxiv (imho the preeminent resource of ML innovations), but is 99% ML/AI and has a lot more detail than arxiv.
A major feature of LPWC is that it's connected to SemOpenAlex, i.e. authors and institutions are disambiguated and connected to LOD.
It's stored in GraphDB 10 and connected by internal federation to SemOpenAlex
https://dtai-kg.github.io/MLSea-KGC/ is an RDFization of Kaggle, OpenML (and some additional data).
The source is at https://github.com/dtai-kg/MLSea-KGC.
It has this breakdown per class: 1.1M software, 940k source code repos, 294k datasets, 408k publications (ScientificWork).
It's stored in Virtuoso 08.03.3329 with 1,445,226,769 triples.
But https://dtai-kg.github.io/MLSeascape/ refers to GraphDB and sends to https://193.190.127.194:7200 (which is a GraphDB specific port), with 13,393,828 triples.
An open version of the paper is https://2024.eswc-conferences.org/wp-content/uploads/2024/04/146640512.pdf.
It mentions LPWC in brief "Linked Papers with Code [18] is a KG that provides information about ML publications from Papers with Code [50] with related metadata such as their datasets, tasks, and evaluations."
Merging these two excellent resources (including ontologies and actual KGs) will be a major boon for the KG and ML communities!
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