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.

介绍了Linked Papers With Code（LPWC），这是一个RDF知识图谱，提供了关于近40万篇机器学习论文的综合、最新的信息，其中包括所涉及的任务、使用的数据集、实施的方法、以及评估和结果，相比非RDF格式的Papers With Code，LPWC不仅能够将机器学习的最新进展转化为RDF格式，还能够实现科学影响力量化和学术关键内容推荐的新方法。作为一个链接开放数据云上的知识图谱，我们提供多种格式的LPWC，包括RDF转储文件、用于直接网页查询的SPARQL端点，以及具有可解析URI和链接到SemOpenAlex、Wikidata和DBLP数据源的数据源。此外，我们还提供知识图谱嵌入，使得LPWC可以直接应用于机器学习应用中。

论文与代码相连：最新的机器学习作为RDF知识图