The Open UniversitySkip to content

Mining Scholarly Publications for Scientific Knowledge Graph Construction

Buscaldi, Davide; Dessì, Danilo; Motta, Enrico; Osborne, Francesco and Reforgiato Recupero, Diego (2019). Mining Scholarly Publications for Scientific Knowledge Graph Construction. In: Extended Semantic Web Conference 2019, 2-6 Jun, Portoroz, Slovenia.

Full text available as:
PDF (Accepted Manuscript) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Download (192kB) | Preview
Google Scholar: Look up in Google Scholar


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 Knowledge Graph. 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, and iii) analyse an automatically generated Knowledge Graph including 10,425 entities and 25,655 relationships in the field of Semantic Web.

Item Type: Conference or Workshop Item
Keywords: knowledge graph; information extraction; ontology; machine learning; scholarly data; research data; research paper
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: 61160
Depositing User: Francesco Osborne
Date Deposited: 14 May 2019 14:28
Last Modified: 15 May 2019 14:12
Share this page:

Download history for this item

These details should be considered as only a guide to the number of downloads performed manually. Algorithmic methods have been applied in an attempt to remove automated downloads from the displayed statistics but no guarantee can be made as to the accuracy of the figures.

Actions (login may be required)

Policies | Disclaimer

© The Open University   contact the OU