Statistical synopses for graph-structured XML databases
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
XRANK: ranked keyword search over XML documents
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Controlling overlap in content-oriented XML retrieval
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Why structural hints in queries do not help XML-retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
DescribeX: Interacting with AxPRE Summaries
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
HiXEval: highlighting XML retrieval evaluation
INEX'05 Proceedings of the 4th international conference on Initiative for the Evaluation of XML Retrieval
Narrowed extended XPath i (NEXI)
INEX'04 Proceedings of the Third international conference on Initiative for the Evaluation of XML Retrieval
| Hi-index | 0.00 |
In XML retrieval, there is often more than one element in the same document that could represent the same focused result. So, a key challenge for XML retrieval systems is to return the set of elements that best satisfies the information need of the end-user in terms of both content and structure. At INEX, there have been numerous proposals for how to incorporate structural constraints and hints into ranking. These proposals either boost the score of or filter out elements that have desirable structural properties. An alternative approach that has not been explored is to rank elements by improving their structural relevance. Structural relevance is the expected relevance of a list of elements, based on a graphical model of how users browse elements within documents. In our approach, we use summary graphs to describe the process of a user browsing from one part of a document to another.In this paper, we develop an algorithm to structurally score retrieval scenarios using structural relevance. The XML retrieval system identifies the candidate scenarios. We apply structural relevance with a given summary model to identify the most structurally relevant scenario. This results in improved system performance. Our approach provides a consistent way to apply different user models to ranking. We also explore the use of score boosting using these models.