A knowledge graph is essentially a semantic network, a graph-based data structure consisting of nodes (Points) and edges (Edges). In the knowledge graph, each node represents an "entity" that exists in the real world, and each edge is a "relationship" between entities. Knowledge graph is the most effective way to represent relationships. Commonly speaking, Knowledge Graph is a network of relationships obtained by connecting all different kinds of information (Heterogeneous Information) together. The knowledge graph provides the ability to analyze problems from a "relationship" perspective.
Like watching a crime movie, where the police will list the relationships between the culprits and the victims, KM is about connecting different pieces of information to form a network of relationships, so that you can find what you need in that network.
Check Baidu, Baidu Google, etc. are using knowledge graph technology, for example, if you search for "the name of the people", Baidu will give you a recommendation on the right side of the characters in the drama, click on the characters to go in, this is "the name of the people" to expand to the network of information relations.
For this technology, there is an American company called Palantir, which played a particularly colorful role in helping the U.S. capture Osama bin Laden, and Palantir's story can be seen here, which explains the principle behind the title of the big data to solve the case.
A philosopher who helped the CIA find bin Laden by 40 seconds /s?__biz=MzI4NzEwOTUwOA==&mid=404872190&idx=1&sn=734fe72a1c3f57109ec5ecc8bce1591a& scene=21#wechat_redirect
An article to read the commercial application process and technical background of the knowledge graph by Lin Jinzhou
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Interpretation of how to solve the case of big data, from Palantir to see the public security big data Apps /articles/dsj20160524001.html
Even AI's "Netflix" Knowledge Graphs have had a 40-year bad time by Machine Mind /articles/2017-12-13
An overview of financial knowledge graphs
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