Computer Science
nowledge Graph
Scholarly Knowledge Graph


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.

W3C Compliance

CS-KG is aligned with the initiative of the Knowledge Graph Construction W3C Community Group for producing benchmarks, resources, and tools to support the semi-automatic generation of knowledge graphs from documents.

Permanent URL


SPARQL Endpoint

The endpoint to query CS-KG and AI-KG via SPARQL.

CS-KG Documentation

Explore the CS-KG Documentation.

CS-KG Schema

Browse the CS-KG Schema.

CS-KG TTL dump

Download CS-KG in TTL format.

CS-KG CSV dump

Download CS-KG in CSV format.

CS-KG Benchmark

Download CS-KG Benchmark.

AI-KG Schema

The AI-KG Schema.

AI-KG dump

Download AI-KG in TTL format.



Danilo Dessì

Department of Mathematics and Computer Science, University of Cagliari (Italy)

Francesco Osborne

Knowledge Media Institute, The Open University, Milton Keynes (UK)

Diego Reforgiato Recupero

Department of Mathematics and Computer Science, University of Cagliari (Italy)

Davide Buscaldi

LIPN, CNRS (UMR 7030), Université Sorbonne Paris Nord, Villetaneuse (France)

Enrico Motta

Knowledge Media Institute, The Open University, Milton Keynes (UK)

Contact Us

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