• Journal of the Korean Society for information Management
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• v.27 no.1
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• pp.61-77
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• 2010

Blogs are now one of the major information resources on the web. The purpose of this study is to enhance the performance of blog retrieval by means of user assigned tags and trackback information. To this end, retrieval experiments were performed with a dataset of 4,908 blog pages together with their associated trackback URLs. In the experiments, text terms, user tags, and network centrality values based on trackbacks were variously combined as retrieval features. The experimental results showed that employing user tags and network centrality values as retrieval features in addition to text words could improve the performance of blog retrieval.

• Journal of the Korea Society of Computer and Information
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• v.16 no.1
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• pp.19-29
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• 2011

As people have heightened attention to blogs that are individual media, a variety rank algorithms was proposed for the blog search. These algorithms was modified for structural features of blogs that differ from typical web sites, and measured blogs' reputations or popularities based on the interaction results like links, comments or trackbacks and reflected in the search system. But actual blog search systems use not only blog-ranks but also search words, a time factor and so on. Nevertheless, those might not produce desirable results. In this paper, we suggest a topic-rank technique, which can find blogs that have significant degrees of association with topics. This technique is a method which ranks the relations between blogs and indexed words of blog posts as well as the topics representing blog posts. The blog rankings of correlations with search words are can be effectively computed in the blog retrieval by the proposed technique. After comparing precisions and coverage ratios of our blog retrieval system which applis our proposed topic-rank technique, we know that the performance of the blog retrieval system using topic-rank technique is more effective than others.