NDLI: Ontology-Based Automatic Classification and Ranking for Web Documents

Content Provider	IEEE Xplore Digital Library
Author	Jun Fang Lei Guo XiaoDong Wang Ning Yang
Copyright Year	2007
Description	Author affiliation: Northwestern Polytech. Univ., Beijing (Jun Fang; Lei Guo; XiaoDong Wang; Ning Yang)
Abstract	The process of Web document classification involves calculating similarities between documents and categories by using the information extracted from them. In recent years, ontology-based Web documents classification method is introduced to solve the problem of classifier training and not considering semantic relations between words in traditional machine learning algorithms. However, previous works on ontology-based web documents classification miss some important issues of automatic ontology construction and ranking of classified documents. In order to solve these problems, this paper proposes an ontology-based web documents classification and ranking method. Firstly, weighted terms set are extracted from web documents, and ontology is build up by using an effective ontology construction method which clarifies and augments an existent ontology; then similarity score between documents and ontology is computed based on WordNet by using Earth Mover's Distance (EMD) method; finally, web documents are assigned to categories according to the similarity score, and a simple ranking method is used to sort the documents in the same categories. The experiment result shows our classification algorithm achieves better precision and recall compare with adaptive KNN method, and is competitive with SVM method, the ranking method also has good performance.
Starting Page	627
Ending Page	631
File Size	216258
Page Count	5
File Format	PDF
ISBN	9780769528748
DOI	10.1109/FSKD.2007.432
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-24
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Earth Machine learning algorithms Automation Laboratories Support vector machine classification Humans Ontologies Automatic control Data mining
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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