Exploring path query results through relevance feedback

Feedback driven data exploration schemes have been implemented for non-structured data (such as text) and document-centric XML collections where formulating precise queries is often impossible. In this paper, we study the problem of enabling exploratory access, through ranking, to data-centric XML. Given a path query and a set of results identified by the system to this query over the data, we consider feedback which captures the user's preference for some features over the others. The feedback can be "positive" or "negative". To deal with feedback, we develop a probabilistic feature significance measure and describe how to use this for ranking results in the presence of dependencies between the path features. We bring together these techniques in AXP, a system for adaptive and exploratory path retrieval. The experimental results show the effectiveness of the proposed techniques.