Exploiting Transitivity in Probabilistic Models for Ontology Learning

View Sample PDF

Author(s): Francesca Fallucchi (University of Rome – Guglielmo Marconi, Italy)and Fabio Massimo Zanzotto (University of Rome – Tor Vergata, Italy)
Copyright: 2012
Pages: 35
Source title: Semi-Automatic Ontology Development: Processes and Resources
Source Author(s)/Editor(s): Maria Teresa Pazienza (University of Roma Tor Vergata, Italy)and Armando Stellato (University of Roma Tor Vergata, Italy)
DOI: 10.4018/978-1-4666-0188-8.ch010

Keywords: Information Science Reference / Knowledge Discovery / Knowledge Management / Library & Information Science

Purchase

View Exploiting Transitivity in Probabilistic Models for Ontology Learning on the publisher's website for pricing and purchasing information.

Abstract

The authors propose probabilistic models for learning ontologies that expand existing ontologies taking into account both corpus-extracted evidence and the structure of the generated ontologies. The model exploits structural properties of target relations such as transitivity during learning. They then propose two extensions of the probabilistic models: a model for learning from a generic domain that can be exploited to extract new information in a specific domain and an incremental ontology learning system that puts human validations in the learning loop. This latter provides a graphical user interface and a human-computer interaction workflow supporting the incremental leaning loop.

The IRMA Community

Research IRM

Exploiting Transitivity in Probabilistic Models for Ontology Learning

Purchase

Abstract

Related Content

IRMA Sponsors