Searching distributed collections with inference networks
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Learning collection fusion strategies
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
XIRQL: a query language for information retrieval in XML documents
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
A study of smoothing methods for language models applied to Ad Hoc information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Relevance score normalization for metasearch
Proceedings of the tenth international conference on Information and knowledge management
Probabilistic models of information retrieval based on measuring the divergence from randomness
ACM Transactions on Information Systems (TOIS)
The SphereSearch engine for unified ranked retrieval of heterogeneous XML and web documents
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Automatic construction of an opinion-term vocabulary for ad hoc retrieval
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Relevance feedback for structural query expansion
INEX'05 Proceedings of the 4th international conference on Initiative for the Evaluation of XML Retrieval
Feedback-Driven structural query expansion for ranked retrieval of XML data
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
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Using separate indices for each element and merging their results has proven to be a feasible way of performing XML element retrieval; however, there has been little work on evaluating how the main method parameters affect the results. We study the effect of using different weighting models for computing rankings at the single index level and using different merging techniques for combining such rankings. Our main findings are that (i) there are large variations on retrieval effectiveness when choosing different techniques for weighting and merging, with performance gains up to 102%, and (ii) although there does not seem to be any best weighting model, some merging schemes perform clearly better than others.