Information Retrieval and Web Search

Markov, Zdravko; Larose, Daniel T.

doi:10.1002/9780470108093.ch1

Data Mining the Web 2006

DOI: 10.1002/9780470108093.ch1

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Information Retrieval and Web Search

Zdravko Markov

Daniel T. Larose

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2014

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Cited by 2 publications

References 10 publications

(14 reference statements)

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Web-Page Recommendation Based on Web Usage and Domain Knowledge

Nguyen

2014

IEEE Trans. Knowl. Data Eng.

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Web-page recommendation plays an important role in intelligent Web systems. Useful knowledge discovery from Web usage data and satisfactory knowledge representation for effective Web-page recommendations are crucial and challenging. This paper proposes a novel method to efficiently provide better Web-page recommendation through semantic-enhancement by integrating the domain and Web usage knowledge of a website. Two new models are proposed to represent the domain knowledge. The first model uses an ontology to represent the domain knowledge. The second model uses one automatically generated semantic network to represent domain terms, Web-pages, and the relations between them. Another new model, the conceptual prediction model, is proposed to automatically generate a semantic network of the semantic Web usage knowledge, which is the integration of domain knowledge and Web usage knowledge. A number of effective queries have been developed to query about these knowledge bases. Based on these queries, a set of recommendation strategies have been proposed to generate Web-page candidates. The recommendation results have been compared with the results obtained from an advanced existing Web Usage Mining (WUM) method. The experimental results demonstrate that the proposed method produces significantly higher performance than the WUM method.

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Web-Page Recommendation Based on Web Usage and Domain Knowledge

Nguyen

2014

IEEE Trans. Knowl. Data Eng.

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Machine Learning Techniques in Web Content Mining: A Comparative Analysis

Anami

Wadawadagi²,

Pagi³

2014

J. Info. Know. Mgmt.

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With incessantly growing amount of information published over Web pages, the World Wide Web (WWW) has become prolific in the field of data mining research. The heterogeneous and semi-structured nature of Web data has made the process of automated discovery a challenging issue. Web Content Mining (WCM) essentially uses data mining techniques to effectively discover knowledge from Web page contents. The intent of this study is to provide a comparative analysis of Machine Learning (ML) techniques available in the literature for WCM. For analysis, the article focuses on issues such as representation techniques, learning methods, datasets used and performance of each method as a criterion. The survey observes that some of the traditional ML algorithms have been efficiently used to work on Web data. Finally, the paper concludes citing some promising issues for further research in this domain.

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Information Retrieval and Web Search

Cited by 2 publications

References 10 publications

Web-Page Recommendation Based on Web Usage and Domain Knowledge

Web-Page Recommendation Based on Web Usage and Domain Knowledge

Machine Learning Techniques in Web Content Mining: A Comparative Analysis

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