In this project, we will investigate ontology-based explainable deep learning (OBDL) algorithms for textual data to identify key factors, concepts, and hypothesis that significantly contribute to the decision made by deep neural networks.
In this project, we will design ontology-based interpretable deep models for consumer complaint explanation and analysis. The main idea of our algorithms is to consider domain knowledge in the design of deep learning models and utilize domain ontologies for explaining the deep learning models and results through casual modeling.
The goal of this project is to design ontology-based interpretable deep models for consumer complaint explanation and analysis.