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Ontology Driven Extraction of Research Processes

  • Vayianos Pertsas
  • Panos Constantopoulos
  • Ion Androutsopoulos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11136)

Abstract

We address the automatic extraction from publications of two key concepts for representing research processes: the concept of research activity and the sequence relation between successive activities. These representations are driven by the Scholarly Ontology, specifically conceived for documenting research processes. Unlike usual named entity recognition and relation extraction tasks, we are facing textual descriptions of activities of widely variable length, while pairs of successive activities often span multiple sentences. We developed and experimented with several sliding window classifiers using Logistic Regression, SVMs, and Random Forests, as well as a two-stage pipeline classifier. Our classifiers employ task-specific features, as well as word, part-of-speech and dependency embeddings, engineered to exploit distinctive traits of research publications written in English. The extracted activities and sequences are associated with other relevant information from publication metadata and stored as RDF triples in a knowledge base. Evaluation on datasets from three disciplines, Digital Humanities, Bioinformatics, and Medicine, shows very promising performance.

Keywords

Ontology population Information extraction Machine learning methodologies Linked data 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Vayianos Pertsas
    • 1
  • Panos Constantopoulos
    • 1
    • 2
  • Ion Androutsopoulos
    • 1
    • 2
  1. 1.Department of InformaticsAthens University of Economics and BusinessAthensGreece
  2. 2.Digital Curation UnitIMSI - Athena Research CentreAthensGreece

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