Because I am involve in an intelligent Taiwan Phone Number question answering-related project at work. I need to understand the relevant knowledge of “Knowledge graph”. As a non-technical b-end product manager. I am somewhat unfamiliar and uncomfortable when I first entered the Taiwan Phone Number ai field. So I read a lot of literature and technical popular science. And also consulted technical students who are doing ai around me. From this. I have a general understanding of some principles of “Knowledge graph”. And organized the following articles. I hope my article can help non-technical product managers. Or students in other positions. To understand what a “Knowledge map” is more easily and quickly.For example. When you use baidu to search for “Yang mi”. The search results will not only show you bao yang mi’s personal information and relate news.
Intelligent Question Answering Taiwan Phone Number
These interpersonal information are Taiwan Phone Number related to “Yang mi” “This keyword does not overlap. But because it has an actual relationship with the entity “Yang mi”. It is all in the search results of “Yang mi”.In terms of intelligent question and answer. The answer will be inferre for you through the knowledge graph. For example. If you search for “Yang mi’s ex-husband”. It will Taiwan Phone Number directly return you the information of “Hawick lau”. Explain knowledge graph from the perspective of product manager For another example. In the online medical industry. When a patient wants to register but does not know which department to register. He can obtain the department information through the pre-diagnostic assistant. The pre-diagnosis assistant is based on the professional medical knowledge map.
We Can Disassemble Multiple Taiwan Phone Number
The knowledge graph is used to stifle the Taiwan Phone Number behaviors such as fraud and credit card cash out in the cradle: through the graph database of the knowledge graph. The correlation analysis of different Taiwan Phone Number individuals and groups is carrie out. Behavior. Such as the ip address of the place that has been visited. The mac address that has been used (including mobile phone. Pc. Wifi. Etc.). The correlation analysis of social networks. Whether there is historical transaction information between bank accounts. Etc.. To determine whether the user is risky behavior exists.From the above figure. We can find that no matter what shape the er diagram transforms into and how different its appearance is. It is a relational network formed by multiple points and multiple lines connecte to each other. Points we call [entities]. Lines we call [relationships].