PIs: Hai Phan, Dejing Dou, Phung Lai

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. We will focus on specific application in consumer complaint explanation using Financial Industry Business Ontology (FIBO), and further develop a new Consumer Complaint Ontology. The applications of this method can be in various domains, such as financial business, biomedicine, and health informatics. We can keep improving the models by taking into account user feedbacks. We will design unsupervised semantic deep learning models by leveraging deep reinforcement learning, e.g., automatically generating consumer complaints – explanations – verifications. A new consistency bound will be developed to guarantee the reliability of each generated explanation.