Linked Papers With Code (LPWC) is an RDF knowledge graph that comprehensively models the research field of machine learning. It contains information about almost 400,000 machine learning publications, including the tasks addressed, the datasets utilized, the methods implemented, and the evaluations conducted, along with their results. The data set is based on Papers With Code and licensed under the CC BY-SA 4.0 license. Furthermore, we provide knowledge graph embeddings for entities and relations represented in LPWC.
What exactly do we provide?
Periodically updated RDF dump files of Linked Papers With Code.
URI resolution of Linked Papers With Code within the Linked Open Data Cloud.
A publicly accessible SPARQL endpoint containing the latest Linked Papers With Code data.
Our knowledge graph (numbers based on LPWC version v1) contains, among others,
Example Use Cases:
Machine Learning Data Analysis: LPWC is a novel scientific knowledge graph covering the current field of machine learning. Complex analyses, such as comparing conferences or detecting new research topics, become possible in this way.
Scholarly LOD Cloud Enrichment: LPWC is highly integrated with the LOD Cloud and connected to multiple data sources, allowing data integration and enhanced research data management according to the FAIR principles.
Academic Recommender Systems: Given the information overload in science, scientific recommender systems are becoming increasingly important. LPWC with its embeddings provides the basis to recommend key scientific content.