Preliminary Experiments using Subjective Logic for the Polyrepresentation of Information Needs
This work provides a practical application and analysis of a theoretical model for polyrepresentation in information retrieval, but it is incremental as it builds on prior work without broad SOTA impact.
The authors tackled the problem of improving retrieval accuracy by combining multiple information object representations, such as user context, using subjective logic, and showed that their model can predict the optimal combination prior to retrieval.
According to the principle of polyrepresentation, retrieval accuracy may improve through the combination of multiple and diverse information object representations about e.g. the context of the user, the information sought, or the retrieval system. Recently, the principle of polyrepresentation was mathematically expressed using subjective logic, where the potential suitability of each representation for improving retrieval performance was formalised through degrees of belief and uncertainty. No experimental evidence or practical application has so far validated this model. We extend the work of Lioma et al. (2010), by providing a practical application and analysis of the model. We show how to map the abstract notions of belief and uncertainty to real-life evidence drawn from a retrieval dataset. We also show how to estimate two different types of polyrepresentation assuming either (a) independence or (b) dependence between the information objects that are combined. We focus on the polyrepresentation of different types of context relating to user information needs (i.e. work task, user background knowledge, ideal answer) and show that the subjective logic model can predict their optimal combination prior and independently to the retrieval process.