Learning Non-taxonomic Relationships Based on Focused Crawler

Qiao Jianzhong

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Library and Information Service ›› 2010, Vol. 54 ›› Issue (18) : 120-125,129.

Learning Non-taxonomic Relationships Based on Focused Crawler

Qiao Jianzhong

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Abstract

In this paper, a novel framework and methodology for learning non-taxonomic relationships based focused crawler is presented. According to the characteristics of ontology learning from the Web, the main methods used in this paper are word frequency, co-occurrence statistics and, K-Means one of partitioning clustering algorithm, without the complex syntax analysis and semi-supervised clustering algorithm such as EM, BIRCH and SOM, and therefore achieves a high degree of automation and efficiency. Study results will be used to analyze and judge the relevance of the topic for focused crawling. The quality of relations learning will be evaluated objectively by the performance in the practical application of the focused crawler.

Key words

ontology learning / non-taxonomic relation / focused crawler / partitioning clustering algorithm / relevance

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Qiao Jianzhong. Learning Non-taxonomic Relationships Based on Focused Crawler[J]. Library and Information Service, 2010, 54(18): 120-125,129

References

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