Knowledge graph is widely used in chat robot, big data risk control, securities investment, intelligent medical treatment, adaptive education, recommendation system and other scenarios, and covers many fields such as pan Internet, finance, government affairs, medical treatment and so on.
To use property graph to express multiple business scenarios, the following challenges are faced:
1. The types of knowledge are diverse, and there are complex and intertwined relationships between entities and in reality
2. The storage problem of massive data
Nebula Graph uses property graph to store the relationship between entities, and It applies the separation of storage and computing architecture to make scaling easier.
By adding recommendation-related factors and data sources, companies can provide users with highly personalized real-time recommendations.
To realize highly personalized real-time recommendation, there are several challenges:
1. Traverse highly correlated complex data in real time
2. Ensure real-time traversal query performance
3. The volume of business data increases rapidly
Nebula Graph provides native graph storage to ensure efficient and real-time traversal of highly correlated complex data, and its shared-nothing architecture ensures the scalability of the system.