Automatic ontology generation from Web tabular structures

Abstract

Turning the current Web into a Semantic Web requires automatic approaches for document annotation, since manual approaches will not scale in general. The focus of the thesis is on automatic transformation of arbitrary table-like structures into knowledge models, i.e., ontologies. The presented work is based on Hurst's table model and consists of a methodology, an accompanying implementation named TARTAR, and a thorough evaluation. The evaluation showed over 80% success rate of automatic transformation of tables into semantic representations and 100% accuracy in the task of query answering over the table contents.