Author: Zen and the Art of Computer Programming
1 Introduction
Knowledge Graph (KG) is a metadata description language based on network structure, entity relationships and attribute descriptions. It can help people understand complex information quickly and accurately, thereby realizing intelligent information retrieval, decision support, question and answer systems, search engines, etc. key technologies in the field. KG is usually composed of multiple interconnected nodes, edges and attributes, and describes the connection between things through triples. Each triple can be considered as a fact, and each node represents A thing, event or idea, each edge represents a connection between nodes. Therefore, KG can be used for applications in various fields such as entity linking, text mining, intelligent question answering, intelligent recommendation, machine learning, statistical analysis, and knowledge reasoning. With the rapid development of artificial intelligence (AI) technology, deep learning models are increasingly used, especially in the field of NLP. More and more researchers are focusing on using NLP technology to analyze text semantics. and model relationships. Traditional rule-based or statistical methods can no longer handle today's complex text semantic association problems well. At the same time, there is a large amount of massive data in the real world. How to effectively use these massive data to extract meaningful knowledge and construct Knowledge graphs and the use of knowledge graphs for intelligent diagnosis and decision support are also a major topic. The following takes smart security as an example, combined with the electronic monitoring system architecture, intelligent security management, machine learning methods, knowledge graph technology, etc. currently deployed in my country, to explain how to grasp the trend of the times and help public security agencies build industry-leading smart security systems.
2. Explanation of basic concepts and terms
(1) Knowledge graph
Knowledge graph is a metadata description language based on network structure, entity relationships and attribute descriptions. It consists of a number of nodes, edges and attributes. It describes the connection between things through triples, and each triple can be regarded as It is a fact that each node represents a thing, event or concept, and each edge represents a connection between nodes. Its main functions include: entity linking, text mining, intelligent