poster

A statistical approach to URL-based web page clustering

Authors:

Inma Hernández,

Carlos R. Rivero,

David Ruiz, and

Rafael CorchueloAuthors Info & Claims

WWW '12 Companion: Proceedings of the 21st International Conference on World Wide Web

April 2012

Pages 525 - 526

https://doi.org/10.1145/2187980.2188109

Published: 16 April 2012 Publication History

Get Access

Abstract

Most web page classifiers use features from the page content, which means that it has to be downloaded to be classified. We propose a technique to cluster web pages by means of their URL exclusively. In contrast to other proposals, we analyze features that are outside the page, hence, we do not need to download a page to classify it. Also, it is non-supervised, requiring little intervention from the user. Furthermore, we do not need to crawl extensively a site to build a classifier for that site, but only a small subset of pages. We have performed an experiment over 21 highly visited websites to evaluate the performance of our classifier, obtaining good precision and recall results.

References

[1]

E. Baykan, M. R. Henzinger, L. Marian, and I. Weber. Purely URL-based topic classification. In WWW, pages 1109--1110, 2009.

Digital Library

Google Scholar

[2]

L. Blanco, N. Dalvi, and A. Machanavajjhala. Highly efficient algorithms for structural clustering of large websites. In WWW, pages 437--446, 2011.

Digital Library

Google Scholar

[3]

I. Hernández, C. Rivero, D. Ruiz, and R. Corchuelo. A tool for link-based web page classification. In CAEPIA, pages 443--452. 2011.

Digital Library

Google Scholar

[4]

M.-Y. Kan and H. O. N. Thi. Fast webpage classification using URL features. In CIKM, pages 325--326, 2005.

Digital Library

Google Scholar

[5]

C. R. Rivero, I. Hernández, D. Ruiz, and R. Corchuelo. Generating SPARQL executable mappings to integrate ontologies. In ER, pages 118--131, 2011.

Digital Library

Google Scholar

[6]

C. R. Rivero, I. Hernández, D. Ruiz, and R. Corchuelo. On benchmarking data translation systems for semantic-web ontologies. In CIKM, pages 1613--1618, 2011.

Digital Library

Google Scholar

Cited By

View all

Bagban TKulkarni P(2022)Clustering of Template-Generated Webpages Using DOM Tree Paths of URLsInternational Journal of Software Innovation10.4018/IJSI.29799410:1(1-24)Online publication date: 6-May-2022
https://dl.acm.org/doi/10.4018/IJSI.297994
Alrashed TPaparas DBenjelloun OSheng YNoy N(2021)Dataset or Not? A Study on the Veracity of Semantic Markup for Dataset PagesThe Semantic Web – ISWC 202110.1007/978-3-030-88361-4_20(338-356)Online publication date: 30-Sep-2021
https://doi.org/10.1007/978-3-030-88361-4_20
Uygun YOguz ROlmezogullari EAktas M(2020)On the Large-scale Graph Data Processing for User Interface Testing in Big Data Science Projects2020 IEEE International Conference on Big Data (Big Data)10.1109/BigData50022.2020.9378153(2049-2056)Online publication date: 10-Dec-2020
https://doi.org/10.1109/BigData50022.2020.9378153
Show More Cited By

Index Terms

A statistical approach to URL-based web page clustering
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees

Recommendations

CALA: An unsupervised URL-based web page classification system

Unsupervised web page classification refers to the problem of clustering the pages in a web site so that each cluster includes a set of web pages that can be classified using a unique class. The existing proposals to perform web page classification do ...
Read More
A framework for incremental deep web crawler based on URL classification
WISM'11: Proceedings of the 2011 international conference on Web information systems and mining - Volume Part II

With the Web grows rapidly, more and more data become available in the Deep Web.But users have to key in a set of keywords in order to access the pages from some web sites. Traditional search engines only index and retrieve Surface Web pages through ...
Read More
URL-based Web Page Classification
IC3K 2014: Proceedings of the International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1

This paper is concerned with the classification of web pages using their Uniform Resource Locators (URLs) only. There is a number of contexts these days in which it is important to have an efficient and reliable classification of a web-page from the URL,...
Read More

Comments

Information & Contributors

Information

Published In

WWW '12 Companion: Proceedings of the 21st International Conference on World Wide Web

April 2012

1250 pages

ISBN:9781450312301

DOI:10.1145/2187980

General Chairs:
Alain Mille
Université de Lyon, France
,
Fabien Gandon
INRIA, France
,
Jacques Misselis
HP, France
,
Program Chairs:
Michael Rabinovich
Case Western Reserve University, USA
,
Steffen Staab
University of Koblenz-Landau, Germany

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 April 2012

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Poster

Conference

WWW 2012

Sponsor:

Univ. de Lyon

WWW 2012: 21st World Wide Web Conference 2012

April 16 - 20, 2012

Lyon, France

Acceptance Rates

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

16
Total Citations
View Citations
442
Total Downloads

Downloads (Last 12 months)9
Downloads (Last 6 weeks)2

Other Metrics

View Author Metrics

Citations

Cited By

View all

Bagban TKulkarni P(2022)Clustering of Template-Generated Webpages Using DOM Tree Paths of URLsInternational Journal of Software Innovation10.4018/IJSI.29799410:1(1-24)Online publication date: 6-May-2022
https://dl.acm.org/doi/10.4018/IJSI.297994
Alrashed TPaparas DBenjelloun OSheng YNoy N(2021)Dataset or Not? A Study on the Veracity of Semantic Markup for Dataset PagesThe Semantic Web – ISWC 202110.1007/978-3-030-88361-4_20(338-356)Online publication date: 30-Sep-2021
https://doi.org/10.1007/978-3-030-88361-4_20
Uygun YOguz ROlmezogullari EAktas M(2020)On the Large-scale Graph Data Processing for User Interface Testing in Big Data Science Projects2020 IEEE International Conference on Big Data (Big Data)10.1109/BigData50022.2020.9378153(2049-2056)Online publication date: 10-Dec-2020
https://doi.org/10.1109/BigData50022.2020.9378153
Rajalakshmi RAravindan C(2018)A Naive Bayes approach for URL classification with supervised feature selection and rejection frameworkComputational Intelligence10.1111/coin.1215834:1(363-396)Online publication date: 15-Jan-2018
https://doi.org/10.1111/coin.12158
Geraci FPapini T(2018)Approximating Multi-class Text Classification Via Automatic Generation of Training ExamplesComputational Linguistics and Intelligent Text Processing10.1007/978-3-319-77116-8_44(585-601)Online publication date: 10-Oct-2018
https://doi.org/10.1007/978-3-319-77116-8_44
Ali FKhan PRiaz KKwak DAbuhmed TPark DKwak K(2017)A Fuzzy Ontology and SVM–Based Web Content Classification SystemIEEE Access10.1109/ACCESS.2017.27685645(25781-25797)Online publication date: 2017
https://doi.org/10.1109/ACCESS.2017.2768564
Cadegnani SGuerra FIlarri Sdel Carmen Rodríguez-Hernández MTrillo-Lado RVelegrakis YAmaro R(2017)Exploiting Linguistic Analysis on URLs for Recommending Web Pages: A Comparative StudyTransactions on Computational Collective Intelligence XXVI10.1007/978-3-319-59268-8_2(26-45)Online publication date: 15-Jun-2017
https://doi.org/10.1007/978-3-319-59268-8_2
Hernández I(2016)Enterprise information integrationAI Communications10.3233/AIC-15067029:2(397-399)Online publication date: 2-Mar-2016
https://doi.org/10.3233/AIC-150670
Hernández IRivero CRuiz DCorchuelo R(2016)CALAJournal of Systems and Software10.1016/j.jss.2016.02.006115:C(130-143)Online publication date: 1-May-2016
https://dl.acm.org/doi/10.1016/j.jss.2016.02.006
Mucha JSnaprud MNietzio A(2016)Web Page Clustering for More Efficient Website Accessibility EvaluationsComputers Helping People with Special Needs10.1007/978-3-319-41264-1_35(259-266)Online publication date: 6-Jul-2016
https://doi.org/10.1007/978-3-319-41264-1_35
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

CALA: An unsupervised URL-based web page classification system

A framework for incremental deep web crawler based on URL classification

URL-based Web Page Classification