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Knowledge Graphs in Action: Graphs for Seamless Data Integration

Bitnine Global Marketing team

Fri Jul 05 2024

Knowledge Graphs in Action: Graphs for Seamless Data Integration

In today's data-driven world, the power of data is essential for organizations to make informed decisions and drive innovation. However, many industries struggle with managing and connecting vast amounts of information intuitively.


This article explores how a research and development (R&D) institution used graph databases to enhance project management and data integration.


Case Study: R&D Institute

The R&D Institute in this scenario collaborates with overseas partner countries on economic development projects. To establish a knowledge management system that intuitively connects various pieces of information, while assessing new technologies and their applicability to new projects, they chose a graph database. Graph databases excel in processing and analyzing data relationships.


The Challenge with Traditional Databases


Before implementing the graph database, the institute faced several challenges:


Fragmented Information: Crucial project information was scattered across multiple Excel tables, making it difficult to understand relationships between different elements


High Maintenance Costs: Managing and updating tables to reflect evolving business information was time-consuming and costly


Complex Data Relationships: Repeated join operations in traditional databases led to performance issues, limiting the ability to conduct in-depth queries necessary for effective data utilization

Figure 1. Table join operation


The graph database transformed project information, making it suitable for constructing a comprehensive knowledge system. This allowed the team to uncover hidden insights, manage data flexibly, and improve efficiency.


The Impact of Graph Technology

Graph technology enhances data connection, management, and utilization. It has been used to create a knowledge portal, improving information retrieval and recommendation systems. Key offerings of graph databases include:


Relationship-Matching System: Establishes connections through knowledge graphs


Property Graph Model: Optimal for representing knowledge information


Schema-less Structure: Easily adds data without predefined schemas


Advanced Analysis Model: Implements predictive algorithms


Intuitive Visualization: Makes it easy to spot hidden patterns in data

Figure 2. Intuitive Graph Data Visualization


Graph databases are flexible, allowing for the addition of nodes, edges, properties, and more without a standardized schema. This flexibility is beneficial when adding attributes for deeper analysis.


Simple Graph Query Language

Figure 3. Simple Graph Query Language


Cypher, a graph query language, enables efficient querying and retrieval of relational property graphs. Unlike traditional databases, which require searching through numerous documents, graph databases connect all data relationships for effortless retrieval.


Competency in Graph Analysis

Graph analysis involves nodes and edges, providing new insights beyond traditional table-type datasets. Algorithms like clustering and centrality can be applied without AI technology, identifying key individuals and relationships within a network.

Figure 4. Centrality Analysis through a graph network analysis


What to Expect from Graph Extension

Managing diverse data types, including emails, PDFs, images, and videos, is challenging with traditional databases. Graph technology overcomes these challenges with its visualization analysis capabilities, enabling the retrieval of large-scale graph data and focused analysis.


The integration of graph technology addresses existing challenges and opens new possibilities, providing a comprehensive view of data and empowering informed decision-making.


Interested in knowing more about Knowledge Graph Implementation? Contact us today!

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