A fuzzy ontology and SVM–based web content classification system

Farman Ali, Pervez Khan, Kashif Riaz, Daehan Kwak, Tamer Abuhmed, Daeyoung Park, Kyung Sup Kwak

Research output: Contribution to journalArticlepeer-review

50 Scopus citations

Abstract

The volume of adult content on the world wide web is increasing rapidly. This makes an automatic detection of adult content a more challenging task, when eliminating access to ill-suited websites. Most pornographic webpage–filtering systems are based on n-gram, naïve Bayes, K-nearest neighbor, and keyword-matching mechanisms, which do not provide perfect extraction of useful data from unstructured web content. These systems have no reasoning capability to intelligently filter web content to classify medical webpages from adult content webpages. In addition, it is easy for children to access pornographic webpages due to the freely available adult content on the Internet. It creates a problem for parents wishing to protect their children from such unsuitable content. To solve these problems, this paper presents a support vector machine (SVM) and fuzzy ontology–based semantic knowledge system to systematically filter web content and to identify and block access to pornography. The proposed system classifies URLs into adult URLs and medical URLs by using a blacklist of censored webpages to provide accuracy and speed. The proposed fuzzy ontology then extracts web content to find website type (adult content, normal, and medical) and block pornographic content. In order to examine the efficiency of the proposed system, fuzzy ontology, and intelligent tools are developed using Protégé 5.1 and Java, respectively. Experimental analysis shows that the performance of the proposed system is efficient for automatically detecting and blocking adult content.

Original languageEnglish
Article number8094233
Pages (from-to)25781-25797
Number of pages17
JournalIEEE Access
Volume5
DOIs
StatePublished - 1 Nov 2017

Keywords

  • Adult content identification
  • Data mining
  • Fuzzy ontology
  • Semantic knowledge
  • SVM

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