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

Aug 3, 2026

Linked Papers With Code (LPWC) introduces an RDF knowledge graph covering nearly 400,000 machine learning publications with structured coverage of tasks, datasets, methods, evaluations, and results. Compared with the non-RDF Papers With Code counterpart, LPWC maps current advances into RDF and supports new approaches for quantifying scientific impact and recommending key scholarly content. LPWC is openly available under CC-BY-SA 4.0, offered as RDF dumps, a SPARQL endpoint, resolvable URIs, and linked sources to SemOpenAlex, Wikidata, and DBLP, plus embedding resources for downstream ML applications.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages5

Reading Time13 min

AccessFree to download

Introduction

Background and Motivation

LPWC Overview

Linked Papers with Code

LPWC Ontology

LPWC Knowledge Graph

Data Transformation

arXiv :2310 .20475v1 [ cs .DL] 31 Oct 2023 Linked Papers With Code: The Latest in Machine Learning as an RDF Knowledge Graph Michael Färber* , David Lamprecht Karlsruhe Institute of Technology (KIT), Institute AIFB, Germany 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](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. Keywords Scholarly Data, Open Science, Ontology Engineering, Machine Learning 1. Introduction Over the years, several scientific knowledge graphs have emerged, including ORKG [1], MAKG [2], and most recently, SemOpenAlex [3] . However, no knowledge graph exists that explicitly targets the modeling of key content in machine learning on a large scale and always up-to-date. On the other hand, Papers with Code (PWC, [https://paperswithcode.com](https://paperswithcode.com)) has emerged as a platform for machine learning publications, code, datasets, methods, and evaluation tables that can be updated by anyone. However, PWC is only available as a web page and a JSON dump without semantic modeling and Linked Open Data (LOD) integration. We introduce Linked Papers With Code (LPWC)– the first RDF knowledge graph that comprehensively models the research field of machine learning using an extensive ontology. Our knowledge graph goes beyond simply lifting to RDF format, for example, by resolving complex data formats through graph modeling and linking entities to other LOD sources such as SemOpenAlex, Wikidata, and DBLP. LPWC consists of 7,935,279 RDF triples as of June 2023 and is available at [https://linkedpaperswithcode.com. It has multiple](https://linkedpaperswithcode.com. It has multiple) applications, from improved management of research data to more effective integration of data across different research ISWC 2023 Posters and Demos: 22nd International Semantic Web Conference, November 6 –10, 2023, Athens, Greece * Corresponding author. $ [michael.faerber@kit.edu](michael.faerber@kit.edu) (M. Färber); [david.lamprecht@student.kit.edu](david.lamprecht@student.kit.edu) (D. Lamprecht) 􀀚 0000-0001-5458-8645 (M. Färber); 0000-0002-9098-5389 (D. Lamprecht) © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0) . CWPEURorkroceshopedings http://ceurISSN 1613-ws-0073.org CEUR Workshop Proceedings ([CEUR-WS.org](CEUR-WS.org)) domains. By incorporating FAIR principles that focus on the availability and reuse of research data and artifacts, we expect LPWC to improve the discoverability and applicability of machine learning research results. We make the code used for knowledge graph creation and embedding generation available online ([https://github.com/davidlamprecht/linkedpaperswithcode](https://github.com/davidlamprecht/linkedpaperswithcode)). In the following, we present LPWC in detail. 2. Linked Papers with Code Linked Papers With Code Ontology. First, we develop an ontology that adheres to the best practices of ontolo

FAQs

What is Linked Papers With Code (LPWC)?

LPWC is an RDF knowledge graph that models key content in machine learning publications, including tasks, datasets, methods, and evaluations, with results captured in structured form.

How does LPWC differ from Papers With Code (PWC)?

LPWC provides semantic modeling and Linked Open Data integration, translating PWC data into RDF and enabling richer linking and scientific impact quantification beyond the web-page and JSON dump formats.

What access formats and tools does LPWC provide?

LPWC is available as RDF dump files, a SPARQL endpoint for web queries, a data source with resolvable URIs linking to SemOpenAlex, Wikidata, and DBLP, and knowledge graph embeddings for ML applications.

Aug 3, 2026

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