A Binary-Categorization Approach for Classifying Multiple-Record Web Documents Using Application Ontologies and a Probabilistic Model

Authors:
Yiu-Kai Ng;June Tang;Michael A. Goodrich
Affiliations:
-;-;-
Venue:
DASFAA '01 Proceedings of the 7th International Conference on Database Systems for Advanced Applications
Year:
2001

Citing 0
Cited 3

Categorisation of web documents using extraction ontologies

International Journal of Metadata, Semantics and Ontologies
Ontology-based automatic classification of web documents

ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
An automatic approach to classify web documents using a domain ontology

PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

Abstract: The amount of information available on the World Wide Web has been increasing dramatically in recent years. To enhance speedy searching and retrieving Web documents of interest, researchers and practitioners have partially relied on various information retrieval techniques. In this paper, we propose a probabilistic model to classify Web documents into relevant documents and irrelevant documents with respect to a particular application ontology, which is a conceptual-model snippet of standard ontologies. Our probabilistic model is based on multivariate statistical analysis and is different from the conventional probabilistic information retrieval models. The experiments we have conducted on a set of representative Web documents indicate that the proposed probabilistic model is promising in binary-categorization of multiple-record Web documents.