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.
Maintainer: Angelo Salatino ([email protected]) from SKM3 team at the Knowledge Media Institute of the Open University https://skm.kmi.open.ac.uk/