Machine learning is prevalent in big data analytics. At present, almost all machine learning systems utilized by academia and industry operates statistically or optimize blindly. The causal logic behind the results remains a black box, limiting the application of machine learning. Starting from a structural approach to causal reasoning, our research uses machine learning to improve application accuracy, and rules to ensure the interpretability of results. At the same time, a new rule system combined with a machine learning model has been put forward. It can support plug-and-play and deal with the potential semantic relationships in data.

We focus on building the logical relationship between machine learning’s input and output through the research of graph data structures. Based on detailed topological structure and association relationships of graph data levels, attributes, and categories, we are committed to effectively revealing the reasoning logic of machine learning in natural language processing, intelligent question answering, semantic model analysis, etc., and to further improve machine learning’s performance and expand its application range.

We are also dedicated to a new type of rule system that combines logic and machine learning models. Prevalent in fields such as data quality and association analysis, this rule system can maintain the logical reasoning relationship between data while using machine learning models to enhance semantic expression capabilities, and can ultimately achieve a real unity of models and logic.