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Mining Scholarly Data for Fine-Grained Knowledge Graph Construction

Buscaldi, Davide; Dessì, Danilo; Motta, Enrico; Osborne, Francesco and Reforgiato Recupero, Diego (2019). Mining Scholarly Data for Fine-Grained Knowledge Graph Construction. In: Proceedings of the Workshop on Deep Learning for Knowledge Graphs (DL4KG2019), pp. 21–30.

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Abstract

Knowledge graphs (KG) are large network of entities and relationships, tipically expressed as RDF triples, relevant to a specific domain or an organization. Scientific Knowledge Graphs (SKGs) focus on the scholarly domain and typically contain metadata describing research publications such as authors, venues, organizations, research topics, and citations. The next big challenge in this field regards the generation of SKGs that also contain a explicit representation of the knowledge presented in research publications. In this paper, we present a preliminary approach that uses a set of NLP and Deep Learning methods for extracting entities and relationships from research publications and then integrates them in a KG. More specifically, we i) tackle the challenge of knowledge extraction by employing several state-of-the-art Natural Language Processing and Text Mining tools, ii) describe an approach for integrating entities and relationships generated by these tools, iii) analyse an automatically generated Knowledge Graph including 10, 425 entities and 25, 655 relationships derived from 12, 007 publications in the field of Semantic Web, and iv) discuss how Deep Learning methods can be applied to overcome some limitations of the current techniques.

Item Type: Conference or Workshop Item
Copyright Holders: 2019 The Authors
ISSN: 1613-0073
Extra Information: Co-located with the 16th Extended Semantic Web Conference 2019 (ESWC 2019)
Keywords: knowledge graph; Semantic Web; knowledge extraction; scholarly data; natural language processing
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 61767
Depositing User: Francesco Osborne
Date Deposited: 17 Jun 2019 09:53
Last Modified: 04 Jul 2019 11:47
URI: http://oro.open.ac.uk/id/eprint/61767
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