Scientific knowledge has been traditionally disseminated and preserved through research articles published in journals, conference proceedings, and online archives. However, this article-centric paradigm has been often criticized for not allowing to automatically process, categorize, and reason on this knowledge. An alternative vision is to generate a semantically rich and interlinked description of the content of research publications.
The Computer Science Knowledge Graph (CS-KG) is a large-scale automatically generated knowledge graph describing 67M statements from 14.5M articles about 24M entities (e.g., tasks, methods, materials, metrics) linked by 219 semantic relations. It was designed to support a large variety of intelligent services for analyzing and making sense of research dynamics, supporting researchers in their daily job, and informing decision of founding bodies and research policy makers.
CS-KG was generated by applying an automatic pipeline that extracts entities and relationships using four tools: DyGIE++, Stanford CoreNLP, the CSO Classifier, and a new PoS Tagger. It then integrates and filters the resulting triples using a combination of deep learning and semantic technologies in order to produce a high quality knowledge graph. This pipeline was evaluated on a manually crafted gold standard yielding competitive results.
CS-KG is available under CC BY 4.0 and can be downloaded as a dump or queried via a SPARQL endpoint.

CS-KG is now replacing the Artificial Intelligence Knowledge Graph (AI-KG). We suggest users to switch to CS-KG that covers a much larger number of concepts and publications. For the sake of compatibility, old versions of AI-KG will still be avaliable in the download section.

For information and questions please contact:
Danilo Dessì – danilo [dot] dessi [at] unica [dot] it
Francesco Osborne – francesco [dot] osborne [at] open [dot] ac [dot] uk