Graphs to Graph Neural Networks: From Fundamentals to Applications — Part 2a: Knowledge Graphs
Isaac: In this post, I will start to learn about knowledge graphs. I also started to use the more powerful model of ChatGPT with the GPT-4 model. I hope the responses are better now. I start by asking ChatGPT to give me some questions to learn about knowledge graphs:
I want to learn about knowledge graphs after learning about graphs. Give me 30 questions that I can use to interact with you and learn about this topic. Consider also learning about deep learning on knowledge graphs.
Let’s dive into the responses and explore them.
1. What is a knowledge graph?
A knowledge graph is a structured representation of knowledge that consists of entities, relationships, and their attributes. It is typically visualized as a graph, where nodes represent entities (e.g., people, places, concepts) and edges represent relationships between these entities. The relationships can be labeled with properties or attributes to provide more context and information.
Knowledge graphs are used to store and organize information in a way that makes it easily accessible and understandable by both humans and machines. They can be employed in various applications such as semantic search, recommendation systems…
