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

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.

HierClasSArt: Knowledge-Aware Hierarchical Classification of Scholarly Articles

“…The research community has been developing methods for several years for generating machine-understandable representations of scientific domain and is providing platforms for annotating scholarly articles [6], designing ontologies for representing scholarly data [15], and building a variety of large-scale KGs [3,6]. For generating these resources, both manual and automatic approaches can be found in the literature.…”

“…A recent effort of human-crafted KG is Open Research Knowledge Graph (ORKG) 4 [6] which facilitates human experts by providing the possibility to upload their scientific papers and provide machine-readable statements about them. On the other hand, automated approaches have been proposed and applied to build the Computer Science Ontology (CSO) [15] and the Artificial Intelligence KG (AI-KG) [3]. The CSO was built by using the Klink-2 algorithm [13] which uses a set of statistical indicators to estimate hierarchical, temporal, and similarity metrics to detect relationships between entities such as papers, authors, venues, and keywords.…”

“…Since its advent, the Linked Open Data (LOD) cloud has constantly been growing containing many KGs about many different domains such as artificial intelligence, biomedical domain, etc. There has been an increase in the studies generating KGs from scholarly articles [3] since many new articles are published everyday which are either peer-reviewed or not (such as on arXiv). It is hard for the experts to organize and stay updated with the newly published articles which leads to the necessity of developing new methods for automatically organizing scientific articles.…”

Proceedings of the Second Workshop on Scholarly Document Processing

“…For example, some studies have attempted to summarize papers on a particular subject (Teufel and Moens, 2002;Qazvinian and Radev, 2008;Bai et al, 2019). The creation of knowledge graphs of scientific papers has also been pro-posed (Dessì et al, 2020), and Gábor et al (2018) proposed an automatic content-analysis method by extracting the semantic relations of entities in abstracts.…”

Task Definition and Integration For Scientific-Document Writing Support

“…For example, some studies have attempted to summarize papers on a particular subject (Teufel and Moens, 2002;Qazvinian and Radev, 2008;Bai et al, 2019). The creation of knowledge graphs of scientific papers has also been pro-posed (Dessì et al, 2020), and Gábor et al (2018) proposed an automatic content-analysis method by extracting the semantic relations of entities in abstracts.…”