Hello everyone, I am Weixue AI. Today I will introduce to you the practical application of knowledge graph 28 - the query and question and answer practical application of knowledge graph based on py2neo's ICD-11 disease classification. Using the ICD-11 disease classification knowledge graph based on py2neo, we can shuttle between various diseases like exploring a biomedical universe. This amazing map can help us reveal the complex and subtle connections between various diseases. Like a professional detective, we can follow and discover new and unknown connections through query and question-and-answer systems.
This system is a powerful and flexible tool that allows users to ask natural language questions and return precise and detailed answers. Imagine you're asking, "What is cardiac arrest?" or "What factors increase the risk of cancer?" The system acts like a know-it-all, never-tired medical expert, standing by to answer your questions.
The query and question and answer system built on the ICD-11 knowledge graph can not only provide in-depth information acquisition, but also promote cross-domain cooperation and exchanges. For example, clinicians, scientists, drug developers, and public health experts can all access the information they need as a basis for in-depth discussion and collaboration.
Table of contents
- Introduction
- Application background
- ICD-11 disease classification data example
- Import py2neo knowledge graph
- Correlation query between disease classification and disease symptoms
- Summarize
1 Introduction
This article introduces how to use the py2neo library and ICD-11 disease classification data to build a knowledge graph, and perform related queries on disease classification and disease symptoms in the graph. We will show the data processing process in detail and how to import data into the knowledge graph.