Harvesting Relational and Structured Knowledge for Ontology Building in the WPro Architecture

  • Daniele Bagni
  • Marco Cappella
  • Maria Teresa Pazienza
  • Marco Pennacchiotti
  • Armando Stellato
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4733)


We present two algorithms for supporting semi-automatic ontology building, integrated in WPro, a new architecturefor ontology learning from Web documents. The first algorithm automatically extracts ontological entities from tables, by using specific heuristics and WordNet-based analysis. The second algorithm harvests semantic relations from unstructured texts using Natural Language Processing techniques. The integration in WPro allows a friendly interaction with the user for validating and modifying the extracted knowledge, and for uploading it into an existing ontology. Both algorithms show promising performance in the extraction process, and offer a practical means to speed-up the overall ontology building process.


Semantic Relation Internal Cell Column Header Unstructured Text Table Type 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Daniele Bagni
    • 1
  • Marco Cappella
    • 1
  • Maria Teresa Pazienza
    • 1
  • Marco Pennacchiotti
    • 2
  • Armando Stellato
    • 1
  1. 1.DISP, University of Rome “Tor Vergata”Italy
  2. 2.Computational Linguistics, Saarland UniversityGermany

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