Knowledge Graphs
GLIDE is built on a knowledge graph, from day one. This gives GLIDE a fundamentally different value proposition than other solutions. Why did our engineering team choose to use a knowledge graph? Having data organized in a graph offers several key advantages over other data storage and retrieval methods.
Here are just a few
Flexible and connected data:
A knowledge graph represents data as a network of interconnected entities and their relationships. It allows the capture of complex relationships between different entities and storage of diverse types of data (such as structured, semi-structured, and unstructured) in a unified framework. This flexibility makes it easier to model and represent real-world scenarios accurately, essential for the complex data GLIDE customers need to process and derive value from.
Semantic querying and reasoning:
Knowledge graphs employ semantic technologies to provide more intelligent querying and reasoning capabilities, essential for GLIDE. A knowledge graph, can be queried using natural language or domain-specific ontologies, allowing for more expressive and context-aware searches.
Contextual insights and recommendations:
By leveraging the rich semantic structure of a knowledge graph, GLIDE can derive contextual insights and generate personalized recommendations for customers. The ability to capture the context of data elements and their relationships enables more accurate and targeted recommendations, leading to improved user experiences and decision-making.
Interoperability and standards:
Knowledge graphs use widely accepted standards, such as RDF (Resource Description Framework) and OWL (Web Ontology Language). These standards facilitate interoperability, enabling data sharing and integration across different systems, domains, and organizations.
Enhanced data integration:
Knowledge graphs facilitate the rapid integration of data from many sources and in many formats. Once ingested, by linking disparate datasets, GLIDE customers can discover new connections and gain a more comprehensive understanding of their data. This integration capability is especially valuable in scenarios where data comes from multiple systems, databases, or APIs as is the case with GLIDE.
Data discovery and exploration:
Knowledge graphs facilitate data discovery and exploration by offering a navigable structure. GLIDE can use this ability to traverse the graph, following relationships between entities to explore related data points. This makes it easier to discover hidden patterns, uncover insights, and gain a holistic view of your data.
Scalability and extensibility:
Knowledge graphs are highly scalable and extensible. New entities, relationships, and attributes can be added without disrupting the existing data structure. This flexibility makes knowledge graphs suitable for evolving data requirements and allows for incremental updates and expansions.
Knowledge graph technology offers the Trigyan engineering team a powerful technology for building GLIDE for customers with a wide range of business and IT challenges. From day one we have focused on finding ways to use knowledge graph technology to organize, query, and derive insights from complex and interconnected data to deliver value to our customers.