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.
Buscaldi, D., Dessì, D., Motta, E., Osborne, F., Reforgiato Recupero, D. (2019). Mining Scholarly Publications for Scientific Knowledge Graph Construction. In The Semantic Web: ESWC 2019. Satellite Events (pp.8-12). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-030-32327-1_2].
Mining Scholarly Publications for Scientific Knowledge Graph Construction
Osborne F;
2019
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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