A vector space model for automatic indexing
Communications of the ACM
A survey on the use of relevance feedback for information access systems
The Knowledge Engineering Review
Combination of evidences in relevance feedback for xml retrieval
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Fine-grained relevance feedback for XML retrieval
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
DB&IR integration: report on the Dagstuhl seminar
ACM SIGMOD Record
Feedback-Driven structural query expansion for ranked retrieval of XML data
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Feedback-driven result ranking and query refinement for exploring semi-structured data collections
Proceedings of the 13th International Conference on Extending Database Technology
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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.