Comparison of Representations of Multiple Evidence Using a Functional Framework for IR

Abstract

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.