Hello everyone, I am Weixue AI. Today I will introduce you to the practical application of knowledge graph 18-the actual application of knowledge graph combined with graph neural network GNN, model building and training. This article will introduce in detail how to combine knowledge graph with graph neural network based on Py2neo (Graph Neural Network, GNN) to implement an application project. We will first import CSV data into Neo4j graph database, and then use GNN for training and prediction.
Article directory:
- Project background and purpose
- Knowledge Graph Introduction
- Introduction to Graph Neural Networks
- Dataset preparation
- Import data into Neo4j using Py2neo
- GNN model construction and training
- GNN model prediction and evaluation
- Summarize
1. Project background and purpose
In many application scenarios, there are complex relationships between data, and knowledge graphs and graph neural networks can be used to better mine potential information in data. This project aims to combine knowledge graph and graph neural network technology to realize an application project, so that readers can understand the application methods and advantages of these two technologies.
2. Introduction to knowledge graph
A knowledge graph is a structured knowledge representation that represents entities (nodes) and their relationships (edges) in the form of a graph. Knowledge graphs can be used to store and retrieve large amounts of data, improving the efficiency of data analysis and mining.
3. Introduction to Graph Neural Networks
Graph Neural Network (GNN) is a class of neural network models specially designed for processing graph data. Unlike traditional neural network models (such as convolutional neural networks and recurrent neural networks) that are suitable for processing grid-structured data (such as images and sequence data) in Euclidean space, GNN is mainly for non-Euclidean graphs. structured data.
The graph is composed of nodes (vertices) and edges (edges), and nodes represent