Title :
Knowledge Representation and Inductive Learning with XML
Author_Institution :
The Australian National University, Canberra, Australia
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.
Keywords :
HTML; Information management; Internet; Knowledge engineering; Knowledge representation; Learning systems; Logic; Machine learning algorithms; Markup languages; XML;
Conference_Titel :
Web Intelligence, 2004. WI 2004. Proceedings. IEEE/WIC/ACM International Conference on
Print_ISBN :
0-7695-2100-2
DOI :
10.1109/WI.2004.10125
