Publication Date

2010-01-01

Availability

Open access

Degree Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Science (Arts and Sciences)

Date of Defense

2010-04-16

First Committee Member

Ubbo Visser - Committee Chair

Second Committee Member

Geoff Sutcliffe - Committee Member

Third Committee Member

Stephan Schuerer - Outside Committee Member

Abstract

An ontology is a formal, explicit specification of a shared conceptualization. Formalizing an ontology for a domain is a tedious and cumbersome process. It is constrained by the knowledge acquisition bottleneck (KAB). There exists a large number of text corpora that can be used for classification in order to create ontologies with the intention to provide better support for the intended parties. In our research we provide a novel unsupervised bottom-up ontology generation method. This method is based on lexico-semantic structures and Bayesian reasoning to expedite the ontology generation process. This process also provides evidence to domain experts to build ontologies based on top-down approaches.

Keywords

An Ontology; Learning; Bayesian Inference

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