Artificial Intelligence Knowledge Graph
Scholarly Knowledge Graph
About

About

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 Artificial Intelligence Knowledge Graph (AI-KG) is a large-scale automatically generated knowledge graph that describes 857,658 research entities. AI-KG includes 14M RDF triples and 1,2M statements extracted from 333K research publications in the field of AI and describes 5 types of entities (e.g., tasks, methods, metrics, materials, others) linked by 27 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.
AI-KG was generated by applying an automatic pipeline that extracts entities and relationships using three tools: DyGIE++, Stanford CoreNLP, and the CSO Classifier. 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.
AI-KG is available under CC BY 4.0 and can be downloaded as a dump or queried via a SPARQL endpoint.

Permanent URL

http://w3id.org/aikg

Resources

SPARQL Endpoint

The endpoint to query AI-KG via SPARQL.

AI-KG dump

Download AI-KG in TTL format.

AI-KG Schema

The AI-KG Schema.

Publications

Team

Danilo Dessì

FIZ Karlsruhe - Leibniz Institute for Information Infrastructure, Germany and Karlsruhe Institute of Technology, Institute AIFB (Germany)

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)

Harald Sack

FIZ Karlsruhe - Leibniz Institute for Information Infrastructure, Germany and Karlsruhe Institute of Technology, Institute AIFB (Germany)

Contact Us

For information and questions please contact:
Danilo Dessì – danilo [dot] dessi [at] fiz-karlsruhe [dot] de
Francesco Osborne – francesco [dot] osborne [at] open [dot] ac [dot] uk