Mining schemas in semi-structured data using fuzzy decision trees

Authors:
Sun Wei;Liu Da-xin
Affiliations:
College of Computer Science and Technology, HARBIN Engineering University, HARBIN, Heilongjiang Province, China;College of Computer Science and Technology, HARBIN Engineering University, HARBIN, Heilongjiang Province, China
Venue:
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part IV
Year:
2005

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Abstract

It is well known that World Wide Web has become a huge information resource. The semi-structured data appears in a wide range of applications, such as digital libraries, on-line documentations, electronic commerce. After we have obtained enough data from WWW, we then use data mining method to mine schema knowledge from the data. Therefore, it is very important for us to utilize schema information effectively. This paper proposes a method of schema mining based on fuzzy decision tree to get useful schema information on the web. This algorithm includes three stages, represented using Datalog, incremental clustering, determining using fuzzy decision tree. Using this algorithm, we can discover schema knowledge implicit in the semi-structured data. This knowledge can make users understand the information structure on the web more deeply and thoroughly. At the same time, it can also provide a kind of effective schema for the querying of web information. In the future, we will further the work on extract association rules using machine learning method and study the clustering method in semi-structured data knowledge discovery.