Collective subjective logic: Scalable uncertainty-based opinion inference

Abstract

Subjective Logic (SL), as one of the state-of-the-art belief models, has been proposed to model an opinion that explicitly deals with its uncertainty. SL offers a variety of operators to update opinions consisting of belief, disbelief, and uncertainty. However, SL operators lack scalability to derive opinions from a large-scale network data due to the sequential procedures of combining two opinions, instead of collective procedures dealing with multiple opinions concurrently. In addition, SL's performance in predicting unknown opinions has been validated only when the uncertainty mass is sufficiently low. To enhance scalability and prediction accuracy of unknown opinions in SL, we take a hybrid approach by combining SL with Probabilistic Soft Logic (PSL). PSL provides collective reasoning with high scalability based on relationships between opinions but does not deal with uncertainty. By taking the merits of both SL and PSL, we propose a probabilistic logic algorithm, called Collective Subjective Logic (CSL) that provides high scalability and high prediction accuracy while dealing with uncertain opinions. Our proposed CSL is generic to deal with uncertain opinions with both high scalability and high prediction accuracy of unknown opinions over a large-scale network dataset. Through the extensive simulation experiments, we validated the outperformance of CSL compared against SL and PSL in terms of prediction accuracy of unknown opinions and algorithmic complexity using Epinions and two road traffic datasets.