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Mining Scholarly Publications for Scientific Knowledge Graph Construction

  • Davide Buscaldi
  • Danilo DessìEmail author
  • Enrico Motta
  • Francesco Osborne
  • Diego Reforgiato Recupero
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11762)

Abstract

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.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Davide Buscaldi
    • 1
  • Danilo Dessì
    • 2
    Email author
  • Enrico Motta
    • 3
  • Francesco Osborne
    • 3
  • Diego Reforgiato Recupero
    • 2
  1. 1.ParisFrance
  2. 2.CagliariItaly
  3. 3.Milton KeynesUK

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