Inference networks for document retrieval
SIGIR '90 Proceedings of the 13th annual international ACM SIGIR conference on Research and development in information retrieval
Dempster-Shafer's theory of evidence applied to structured documents: modelling uncertainty
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
XML retrieval: what to retrieve?
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Keyword Proximity Search in XML Trees
IEEE Transactions on Knowledge and Data Engineering
XCluster Synopses for Structured XML Content
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Identifying meaningful return information for XML keyword search
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Effective keyword search for valuable lcas over xml documents
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Query biased snippet generation in XML search
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Novelty and diversity in information retrieval evaluation
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Statistical Language Models for Information Retrieval A Critical Review
Foundations and Trends in Information Retrieval
Possibilistic networks for information retrieval
International Journal of Approximate Reasoning
Data integration for the relational web
Proceedings of the VLDB Endowment
Towards a framework for attribute retrieval
Proceedings of the 20th ACM international conference on Information and knowledge management
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In this paper, we are interested in aggregated search in structured XML documents. We present a structured information retrieval model based on the Bayesian networks theory. Query-terms and terms-elements relations are modeled through probability. In this model, the user's query starts a propagation process to recover the XML elements. Thus, instead of retrieving a whole document or a list of disjoint elements that are likely to answer partially the query, we attempt to built a virtual document that aggregates a set of elements, that are relevant all together. We evaluated our approach using the INEX 2009 collection and presented some empirical results for evaluating the impact of the aggregation approach.