A machine learning approach to rapid development of XML mapping queries
Atsuyuki Morishima, Hiroyuki Kitagawa, Akira Matsumoto · 2004
We present XLearner, a novel tool that helps the rapid development of XML mapping queries written in XQuery. XLearner is novel in that it learns XQuery queries consistent with given examples (fragments) of intended query results. XLearner combines known learning techniques, incorporates mechanisms to cope with issues specific to the XQuery learning context, and provides a systematic way for the semiautomatic development of queries. We describe the XLearner system. It presents algorithms for learning various classes of XQuery, shows that a minor extension gives the system a practical expressive power, and reports experimental results to demonstrate how XLearner outputs reasonably complicated queries with only a small number of interactions with the user.