Semantic Based Highly Accurate Autonomous Decentralized URL Classification System for Web Filtering

Author

Mahmood, Khalid ; Takahashi, Hironao ; Raza, Asif ; Qaiser, Asma ; Farooqui, Aadil

Author_Institution

Dept. of Comput. Sci. & Eng., Oakland Univ., Rochester, MI, USA

fYear

2015

fDate

25-27 March 2015

Firstpage

17

Lastpage

24

Abstract

Currently cyberspace has got about one billion registered websites, and it is imperative to accurately categorize voluminous number of website/URLs for the purpose of URL filtering and marketing segmentation. This paper presents autonomous decentralized semantic based large-scale URL/web classification system for web filtering using Yago2s and DS-onto knowledgebase. As many predefined categories are highly overlapping or semantically similar, proposed word sense disambiguation algorithm along with inference engine design brings high accuracy for classification of URLs in to 120 different categories. Evaluation results show that it achieves 90-93% of accuracy which is much higher than that obtained by currently used URL classification systems.

Keywords

classification; inference mechanisms; information filtering; natural language processing; semantic Web; DS; URL filtering; Web classification system; Web filtering; Web sites; Yago2s; inference engine design; marketing segmentation; semantic based autonomous decentralized URL classification system; word sense disambiguation algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Autonomous Decentralized Systems (ISADS), 2015 IEEE Twelfth International Symposium on

Conference_Location

Taichung

Print_ISBN

978-1-4799-8260-8

Type

conf

DOI

10.1109/ISADS.2015.34

Filename

7098233