Our Technology

Why a Knowledge Graph?

A knowledge graph is a graph-structured representation of information where nodes represent entities and edges represent relationships between them. This model:

• Enables intuitive representation of complex real-world systems, modeling hierarchies, taxonomies, networks, and dependencies.

• Encodes both structural and semantic context, enabling complex reasoning and inference.

• Supports graph algorithms for network analytics, such as centrality, clustering, and pathfinding.

• Facilitates the integration of heterogeneous data sources, helping unify fragmented or siloed information into a coherent whole.

Why a Graph Database?

A graph database provides the infrastructure for storing and querying graph-structured data. Beyond supporting the modeling advantages outlined above, graph databases offer performance and flexibility benefits tailored to highly interconnected data, including:

• Flexible, schema-optional structure that can evolve as data models change, unlike the rigid schemas of relational databases.

• Faster querying of interconnected data at scale through direct node–edge traversal, avoiding the latency introduced by JOIN operations in relational databases.

• Reduced memory and computational overhead by explicitly storing relationships between entities, avoiding the need for runtime reconstruction of connections via JOINs and the associated creation of intermediate memory-intensive structures.

• Use of mathematical graph theory to support efficient execution of complex queries, such as multi-hop traversals and pathfinding across linked entities.