ACM: Article Content Miner for Assessing the Quality of Scientific Output

  • Andrea Giovanni Nuzzolese
  • Silvio Peroni
  • Diego Reforgiato Recupero
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 641)

Abstract

This paper presents the Article Content Miner (a.k.a. ACM), i.e., a method for processing the research papers in PDF format available for the 2016 edition of the Semantic Publishing Challenge in order to extract relevant semantic data and publish them in a RDF triplestore according to the Semantic Publishing And Referencing (SPAR) Ontologies (http://www.sparontologies.net). In particular, the extraction of all the information needed for addressing the queries of the second task of the challenge (https://github.com/ceurws/lod/wiki/SemPub16_Task2) is guaranteed by ACM by using techniques based on Natural Language Processing (i.e., Combinatory Categorial Grammar, Discourse Representation Theory, Linguistic Frames), Semantic Web technologies and good Ontology Design practices (i.e., Content Analysis, Ontology Design Patterns, Discourse Referent Extraction and Linking, Topic Extraction).

Keywords

Machine reading SPAR Ontologies Semantic Publishing 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Andrea Giovanni Nuzzolese
    • 1
  • Silvio Peroni
    • 2
  • Diego Reforgiato Recupero
    • 3
  1. 1.Semantic Technology LaboratoryISTC-CNRRomeItaly
  2. 2.Digital and Semantic Publishing Laboratory, Department of Computer Science and EngineeringUniversity of BolognaBolognaItaly
  3. 3.Department of Mathematics and Computer ScienceUniversity of CagliariCagliariItaly

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