In the second year, we will focus on domain ontology-specific deep learning methods, integrating contemporary deep learning model designs with the existing SMASH healthcare ontology and corresponding data, to structure both model input and potentially output in order to develop meaningful semantic explanations that compare favorably with the state of the art.
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.
Deep learning (DL) has in recent years been widely used in computer vision and natural language processing (NLP) applications due to its superior performance. However, while images and natural languages are known to be rich in structures expressed, for example, by grammar rules, DL has so far not been capable of explicitly representing and enforcing such structures [Huang18]. In this project, we propose an approach to bridging this gap by exploiting tensor product representations (TPR) [Smolensky90a, Smolensky90b], a structured neural-symbolic model developed in cognitive science, aiming to integrate DL with explicit language rules, logical rules, or rules that summarize the human knowledge about the subject.
The goal of this project is to create a novel “Semantic Deep Mining” method to analyze the electronic health records (EHRs) of real patients.
The goal of this project is to design ontology-based interpretable deep models for consumer complaint explanation and analysis.