Using the Semantic Web & Data Mining Schemes to Filter
Pornography Webpages
Abstract
Today's Internet is filled with a variety of pornographic information. Protecting the
adolescents and children from the influence of illegal information and pornography is a
crucial issue.At present, most filtering systems focus on percolating pornographic and violent
information. However, a variety of legal and normal websites are also filtered out by these
systems even though the websites contain sex and violence related information.Therefore, this
study utilized the Semantic Web and data mining techniques to distinguish between legal and
sex-violence websites and improved the accuracy of information filtering. The system
adopted Data mining technology TF / IDF for weight analysis. The browse files termed by the
CKIP-off, and then calculated each word in the document off the weight value. Finally, the
system selected the line weight default value of breaking the word and the ontology
knowledge base for comparison.Through the ontological structure of the system, this study
checked the file association between the breaking words. Then, this study further inferred the
implicit rules by Jena in order to justify the browsing files of websites. Finally, this system,
based on the justification results, presented the illustrations to inform the problems in user’s
browsing information.
Keyword: Semantic Web、Ontology、CKIP、TF/IDF Weight Analysis。