background
In 2022, with the deepening of natural language processing, I gradually began to have a strong interest in the application of knowledge graphs in the fields of question answering, search, and recommendation. I have also learned about knowledge graphs through books, blog posts, papers, etc., and have a deep understanding of the development of Chinese knowledge graphs in various fields through Chinese open knowledge graphs. Knowledge graph plays a relatively important role in cognitive intelligence, and it also gave me a new understanding of knowledge graph and its related technologies (NLP, graph neural network, etc.).
If you want to understand the research status of a certain field, authoritative books and courses are the best. Of course, reading review articles in the corresponding field is also a good choice. I learned that Chen Huajun of Zhejiang University has a book "Introduction to Knowledge Mapping". This book also has a related course in Chinese University MOOC: Introduction to Knowledge Graph . So the study started, and now I will review and summarize the content of the study.
The courseware corresponding to the course can be obtained by replying in my subscription account: " Introduction to Knowledge Mapping-Zhejiang University ", and the corresponding content can also be viewed in my summary.
The corresponding explanation video content is as follows:
[Knowledge Graph Theory] (Zhejiang University 2022 Knowledge Graph Course) Lecture 1 - Overview of Knowledge Graph
language and knowledge
The human brain relies on what it has learned to think, reason, and understand language.
early artificial