Score-Based Learning of Graphical Event Models with Background Knowledge Augmentation
Debarun Bhattacharjya, Tian Gao, Dharmashankar Subramanian, Xiao Shou
Abstract
Graphical event models (GEMs) are representations of temporal point process dynamics between different event types. Many real-world applications however involve limited event stream data, making it challenging to learn GEMs from data alone. In this paper, we introduce approaches that can work together in a score-based learning paradigm, to augment data with potentially different types of background knowledge. We propose novel scores for learning an important parametric class of GEMs; in particular, we propose a Bayesian score for leveraging prior information as well as a more practical simplification that involves fewer parameters, analogous to Bayesian networks. We also introduce a framework for incorporating easily assessed qualitative background knowledge from domain experts, in the form of statements such as `event X depends on event Y' or `event Y makes event X more likely'. The proposed framework has Bayesian interpretations and can be deployed by any score-based learner. Through an extensive empirical investigation, we demonstrate the practical benefits of background knowledge augmentation while learning GEMs for applications in the low-data regime.
BibTeX
@article{Bhattacharjya_Gao_Subramanian_Shou_2023, title={Score-Based Learning of Graphical Event Models with Background Knowledge Augmentation}, volume={37}, url={https://ojs.aaai.org/index.php/AAAI/article/view/26437}, DOI={10.1609/aaai.v37i10.26437}, abstractNote={Graphical event models (GEMs) are representations of temporal point process dynamics between different event types. Many real-world applications however involve limited event stream data, making it challenging to learn GEMs from data alone. In this paper, we introduce approaches that can work together in a score-based learning paradigm, to augment data with potentially different types of background knowledge. We propose novel scores for learning an important parametric class of GEMs; in particular, we propose a Bayesian score for leveraging prior information as well as a more practical simplification that involves fewer parameters, analogous to Bayesian networks. We also introduce a framework for incorporating easily assessed qualitative background knowledge from domain experts, in the form of statements such as `event X depends on event Y’ or `event Y makes event X more likely’. The proposed framework has Bayesian interpretations and can be deployed by any score-based learner. Through an extensive empirical investigation, we demonstrate the practical benefits of background knowledge augmentation while learning GEMs for applications in the low-data regime.}, number={10}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Bhattacharjya, Debarun and Gao, Tian and Subramanian, Dharmashankar and Shou, Xiao}, year={2023}, month={Jun.}, pages={12189-12197} }