Prontolearn: Unsupervised lexico-semantic ontology generation using probabilistic methods

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations


Formalizing an ontology for a domain manually is well-known as a tedious and cumbersome process. It is constrained by the knowledge acquisition bottleneck. Therefore, researchers developed algorithms and systems that can help to automatize the process. Among them are systems that include text corpora for the acquisition. Our idea is also based on vast amount of text corpora. Here, we provide a novel unsupervised bottom-up ontology generation method. It is based on lexico-semantic structures and Bayesian reasoning to expedite the ontology generation process. We provide a quantitative and two qualitative results illustrating our approach using a high throughput screening assay corpus and two custom text corpora. This process could also provide evidence for domain experts to build ontologies based on top-down approaches.

Original languageEnglish (US)
Pages (from-to)25-36
Number of pages12
JournalCEUR Workshop Proceedings
StatePublished - 2010
Event6th International Workshop on Uncertainty Reasoning for the Semantic Web, URSW 2010 - Collocated with the 9th International Semantic Web Conference, ISWC 2010 - Shanghai, China
Duration: Nov 7 2010Nov 7 2010


  • Ontology learning
  • Ontology modeling
  • Probabilistic methods

ASJC Scopus subject areas

  • Computer Science(all)


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