SIGIR '89 Proceedings of the 12th annual international ACM SIGIR conference on Research and development in information retrieval
Inference networks for document retrieval
SIGIR '90 Proceedings of the 13th annual international ACM SIGIR conference on Research and development in information retrieval
Evaluation of an inference network-based retrieval model
ACM Transactions on Information Systems (TOIS) - Special issue on research and development in information retrieval
Inference networks for document retrieval
Inference networks for document retrieval
Probabilistic models in information retrieval
The Computer Journal - Special issue on information retrieval
Index expression belief networks for information disclosure
International Journal of Expert Systems
Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Applying Bayesian networks to information retrieval
Communications of the ACM
Pivoted document length normalization
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
SIGIR '96 Proceedings of the 19th 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
Fuzzy sets as a basis for a theory of possibility
Fuzzy Sets and Systems
Evaluating evaluation measure stability
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Link-based and content-based evidential information in a belief network model
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Information Retrieval
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
A Layered Bayesian Network Model for Document Retrieval
Proceedings of the 24th BCS-IRSG European Colloquium on IR Research: Advances in Information Retrieval
Information Processing and Management: an International Journal - Modelling vagueness and subjectivity in information access
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems - Intelligent information systems
Information retrieval system evaluation: effort, sensitivity, and reliability
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
A model for information retrieval based on possibilistic networks
SPIRE'05 Proceedings of the 12th international conference on String Processing and Information Retrieval
AMR'05 Proceedings of the Third international conference on Adaptive Multimedia Retrieval: user, context, and feedback
Document length normalization using effective level of term frequency in large collections
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
Using Bayesian networks theory for aggregated search to XML retrieval
Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics
Possibilistic model for aggregated search in XML documents
International Journal of Intelligent Information and Database Systems
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This paper proposes an information retrieval (IR) model based on possibilistic directed networks. The relevance of a document w.r.t a query is interpreted by two degrees: the necessity and the possibility. The necessity degree evaluates the extent to which a given document is relevant to a query, whereas the possibility degree evaluates the reasons of eliminating irrelevant documents. This new interpretation of relevance led us to revisit the term weighting scheme by explicitly distinguishing between informative and non-informative terms in a document. Experiments carried out on three standard TREC collections show the effectiveness of the model.