Knowledge Representation and Inductive Learning with XML

Authors:
Xiaobing Wu
Affiliations:
The Australian National University, Canberra, Australia
Venue:
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
Year:
2004

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Abstract

This paper presents a novel knowledge representation method and learning system for XML documents. The traditional machine learning methods which use attribute-value languages are not suitable for representing XML documents due to their complex structures. In this paper, we propose a decision-tree algorithm for XML learning, which is based on a rich representation language for structured data and driven by precision/recall heuristic.