SIGIR '96 Proceedings of the 19th 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
A unified mathematical definition of classical information retrieval
Journal of the American Society for Information Science
Application of aboutness to functional benchmarking in information retrieval
ACM Transactions on Information Systems (TOIS)
Modern Information Retrieval
Logic and Uncertainty in Information Retrieval
ESSIR '00 Proceedings of the Third European Summer-School on Lectures on Information Retrieval-Revised Lectures
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The combination of sources of evidence is an important subject of research in information retrieval and can be a good strategy for improving the quality of rankings. Another active research topic is modeling and is one of the central tasks in the development of information retrieval systems. In this paper, we analyze the combination of multiple evidence using a functional framework, presenting two case studies of the use of the framework to combine multiple evidence in contexts bayesian belief networks and in the vector space model. This framework is a meta-theory that represents IR models in a unique common language, allowing the representation, formulation and comparison of these models without the need to carry out experiments. We show that the combination of multiple evidence in the bayesian belief network can be carried at in of several ways, being that each form corresponds to a similarity function in the vector model. The analysis of this correspondence is made through the functional framework. We show that the framework allows us to design new models and helps designers to modify these models to extend them with new evidence sources.