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Integrating Knowledge Graphs for Analysing Academia and Industry Dynamics

Angioni, Simone; Salatino, Angelo; Osborne, Francesco; Reforgiato Recupero, Diego and Motta, Enrico Integrating Knowledge Graphs for Analysing Academia and Industry Dynamics. In: 1st​ Workshop on Scientific Knowledge Graphs, 25 Aug 2020, Lyon, France.

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Abstract

Academia and industry are constantly engaged in a joint effort for producing scientific knowledge that will shape the society of the future. Analysing the knowledge flow between them and understanding how they influence each other is a critical task for researchers, governments, funding bodies, investors, and companies. However, current corpora are unfit to support large-scale analysis of the knowledge flow between academia and industry since they lack of a good characterization of research topics and industrial sectors. In this short paper, we introduce the Academia/Industry DynAmics (AIDA) Knowledge Graph, which characterizes 14M papers and 8M patents according to the research topics drawn from the Computer Science Ontology. 4M papers and 5M patents are also classified according to the type of the author's affiliations (academy, industry, or collaborative) and 66 industrial sectors (e.g., automotive, financial, energy, electronics) obtained from DBpedia. AIDA was generated by an automatic pipeline that integrates several knowledge graphs and bibliographic corpora, including Microsoft Academic Graph, Dimensions, English DBpedia, the Computer Science Ontology, and the Global Research Identifier Database.

Item Type: Conference or Workshop Item
Copyright Holders: 2020 The Authors
Keywords: scholarly data; knowledge graph; topic detection; bibliographic data; scholarly ontologies; research dynamics
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
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Item ID: 70715
Depositing User: Angelo Salatino
Date Deposited: 08 Jun 2020 09:56
Last Modified: 11 Jun 2020 20:32
URI: http://oro.open.ac.uk/id/eprint/70715
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