Advancing Topic Ontology Learning through Term Extraction

  • Blaž Fortuna
  • Nada Lavrač
  • Paola Velardi
Conference paper

DOI: 10.1007/978-3-540-89197-0_57

Volume 5351 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Fortuna B., Lavrač N., Velardi P. (2008) Advancing Topic Ontology Learning through Term Extraction. In: Ho TB., Zhou ZH. (eds) PRICAI 2008: Trends in Artificial Intelligence. PRICAI 2008. Lecture Notes in Computer Science, vol 5351. Springer, Berlin, Heidelberg

Abstract

This paper presents a novel methodology for topic ontology learning from text documents. The proposed methodology, named OntoTermExtraction (Term Extraction for Ontology learning), is based on OntoGen, a semi-automated tool for topic ontology construction, upgraded by using an advanced terminology extraction tool in an iterative, semi-automated ontology construction process. This process consists of (a) document clustering to find the nodes in the topic ontology, (b) term extraction from document clusters, (c) populating the term vocabulary and keyword extraction, and (d) choosing the concept names by comparing the best-ranked terms with the extracted keywords. The approach was successfully used for generating the ontology of topics in Inductive Logic Programming, learned semi-automatically from papers indexed in the ILPnet2 publications database.

Keywords

Topic ontology ontology construction term extraction 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Blaž Fortuna
    • 1
  • Nada Lavrač
    • 1
    • 2
  • Paola Velardi
    • 3
  1. 1.Jožef Stefan InstituteLjubljanaSlovenia
  2. 2.University of Nova GoricaGoricaSlovenia
  3. 3.Universita di Roma “La Sapienza”RomaItaly