Practical application of knowledge graph 13-Intelligent question answering system in the field of medical diseases, realizing symptom question answering and supporting data expansion

Hello everyone, I am Weixue AI. Today, I will introduce to you the practical application of knowledge graph 13-intelligent question answering system in the field of medical diseases, which realizes symptom question answering and supports data expansion. This article will introduce in detail how to use the Python library Py2neo to build a knowledge map in the medical field and import the data into the Neo4j graph database. We will provide some data samples in the medical field and show how to use code to create and query knowledge graphs.

Article directory:

1. introduce
2. Environmental preparation
3. Data samples in the medical field
4. Create a Neo4j graph database
5. Import data using Py2neo
6. Query Knowledge Graph
7. Summarize

1 Introduction

A knowledge graph is a graph data structure used to represent relationships between entities. In the medical field, knowledge graphs can help us better understand the connections between diseases, symptoms, treatments, and more. This article will use the Py2neo library and the Neo4j graph database to build a simple knowledge graph in the medical field.

2. Environment preparation

First, we need to install Neo4j and Py2neo. Make sure you have installed Python and pip, then follow the steps below to install the required software:

1. Install Neo4j: Please visit Neo4j official website to download and install Neo4j Community Edition.
2. Install Py2neo: Install the Py2neo library using pip:

pip install py2neo

3. Data samples in the medical field

We will provide some data samples from the medical field, including diseases, symptoms, and treatments. The data is stored in CSV format, with a total of