A bottom-up approach for XML documents classification

ABSTRACT

Extensible Markup Language (XML) is a simple and flexible text format derived from SGML [1]. It has been widely accepted as one of the crucial components in many information retrieval related applications, such as XML databases, web services, etc. One of the reasons for its wide acceptance is its customized format during data transmission or data storage. Classification is an important data mining task, which aims to assign unknown objects to classes which best characterize them. In this paper, we propose a method to classify XML documents under the assumption that they do not have a common schema, which may or may not be available. Our method is similarity-based. Its main characteristics is its way to handle the roles played by texts and the structural information. Unlike most existing methods, we use a bottom-up approach, i.e., we start from the text first, and then embed the structural information. This is based on the observation that in XML documents with diversified tag structures, the most informative information are carried by the terms in the texts. Our experiments show that this strategy can achieve a better performance than the existing methods for documents from sources that exhibit heterogeneous structures.

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications
          Subjects: Data mining

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.7 Natural Language Processing
          Subjects: Text analysis

Keywords:
classification, discretization, key path, key term, mutual information, normalized occurrence