Dynamic transformation of prior knowledge into Bayesian models for data streams
This addresses the challenge of leveraging external knowledge sources in streaming data applications, representing an incremental improvement over existing Bayesian models.
The authors tackled the problem of effectively incorporating prior knowledge into Bayesian models for streaming data environments, proposing a novel framework that outperforms existing methods by a large margin and enables better generalization on extremely short text.
We consider how to effectively use prior knowledge when learning a Bayesian model from streaming environments where the data come infinitely and sequentially. This problem is highly important in the era of data explosion and rich sources of precious external knowledge such as pre-trained models, ontologies, Wikipedia, etc. We show that some existing approaches can forget any knowledge very fast. We then propose a novel framework that enables to incorporate the prior knowledge of different forms into a base Bayesian model for data streams. Our framework subsumes some existing popular models for time-series/dynamic data. Extensive experiments show that our framework outperforms existing methods with a large margin. In particular, our framework can help Bayesian models generalize well on extremely short text while other methods overfit. The implementation of our framework is available at https://github.com/bachtranxuan/TPS.git.