AI-KG: an Automatically Generated Knowledge Graph of Artificial Intelligence

Dessì, Danilo; Osborne, Francesco; Reforgiato Recupero, Diego; Buscaldi, Davide; Motta, Enrico and Sack, Harald (2020). AI-KG: an Automatically Generated Knowledge Graph of Artificial Intelligence. In: The Semantic Web – ISWC 2020 (Pan, J.Z.; Tamma, V.; d’Amato, C.; Janowicz, K.; Fu, B.; Polleres, A.; Seneviratne, O. and Kagal, L. eds.), Springer, pp. 127–143.

DOI: https://doi.org/10.1007/978-3-030-62466-8_9

Abstract

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. In this paper, we present the Artificial Intelligence Knowledge Graph (AI-KG), a large-scale automatically generated knowledge graph that describes 820K research entities. AI-KG includes about 14M RDF triples and 1.2M reified statements extracted from 333K research publications in the field of AI, and describes 5 types of entities (tasks, methods, metrics, materials, others) linked by 27 relations. AI-KG has been designed to support a variety of intelligent services for analyzing and making sense of research dynamics, supporting researchers in their daily job, and helping to inform decision-making in funding bodies and research policymakers. AI-KG has been 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.

Viewing alternatives

Download history

Metrics

Public Attention

Altmetrics from Altmetric

Number of Citations

Citations from Dimensions

Item Actions

Export

About