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From Publications to Knowledge Graphs

  • Panos ConstantopoulosEmail author
  • Vayianos Pertsas
Conference paper
  • 14 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1197)

Abstract

We address the task of compiling structured documentation of research processes in the form of knowledge graphs by automatically extracting information from publications and associating it with information from other sources. This challenge has not been previously addressed at the level described here. We have developed a process and a system that leverages existing information from DBpedia, retrieves articles from repositories, extracts and interrelates various kinds of named and non-named entities by exploiting article metadata, the structure of text as well as syntactic, lexical and semantic constraints, and populates a knowledge base in the form of RDF triples. An ontology designed to represent scholarly practices is driving the whole process. Rule -based and machine learning- based methods that account for the nature of scientific texts and a wide variety of writing styles have been developed for the task. Evaluation on datasets from three disciplines, Digital Humanities, Bioinformatics, and Medicine, shows very promising performance.

Keywords

Information extraction Process mining Knowledge base creation Machine learning Ontology population 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of InformaticsAthens University of Economics and BusinessAthensGreece
  2. 2.Digital Curation UnitIMSI-Athena Research CentreMarousiGreece

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