Graph DB applies past knowledge and experience into an ‘intelligence’ that achieves greater tasks.
The intelligence graph makes use of necessary information to help ease customers’ decision-making.
Intelligence Graph®
CASE STUDIES
Knowledge Graph in Manpower Matching System
In order to manage ever-increasing data effectively and efficiently, the Center for International Development (CID) of the Korea Development Institute (KDI) established an advanced knowledge management system with Bitnine’s AgensGraph. Reinforced with an optimal graph query language, visualization tool, and graph algorithms, the knowledge graph elevated the value of data and evolved the knowledge management system to another level. The newly built manpower matching system intuitively connected relationships between various data accumulated during overseas projects that KDI experts have participated in.
AI-based Personalized Learning Recommendation
Company E used AgensGraph to provide fully personalized education services for young students. This service took into account each student’s academic achievement, behavioral patterns, and personal preferences, and then suggested an effective way to study. Unlike simply determining students’ progress based on their test scores, this service provided a conceptual framework of the school subjects in a network form and recommended the most effective course for each individual student.
Healthcare Data Platform
Adapting the graph model to handle the complexity of patient data lineage enabled the healthcare data platform to create a lifecycle view of the patient record. With AgensGraph, InteropX is able to present intuitive visualization when analyzing longitudinal patient records without having to know database query skills.
Building Explainable AI (XAI) with Graph Database
Explainable AI models with graph technology are considered to be essential because graph analytics is suitable for calculating and displaying evidence by explaining with graph visualization when needed. Graph-based AI is able to provide the necessary transparency to prove the system is working effectively. The explainable AI is vital for increasing accuracy in preventing phone scams, detecting fraudulent transactions, and money laundering.
