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.

翻译：本文介绍带代码链接论文（LPWC），这是一个RDF知识图谱，涵盖近40万篇机器学习出版物的全面最新信息，包括研究任务、所用数据集、实现方法、评估过程及其结果。与基于非RDF的Papers With Code相比，LPWC不仅将机器学习领域的最新进展转换为RDF格式，还实现了科学影响力量化与学术关键内容推荐的新范式。LPWC可通过https://linkedpaperswithcode.com 公开访问，采用CC-BY-SA 4.0许可协议。作为关联开放数据云中的知识图谱，我们提供多种格式的LPWC数据，包括RDF转储文件、可直接进行网络查询的SPARQL端点，以及具有可解析URI的数据源，并与SemOpenAlex、Wikidata和DBLP等数据源建立链接。此外，我们还提供知识图谱嵌入，使LPWC能够直接应用于机器学习应用场景。