Knowledge Graph (KG)

The Knowledge Graph (KG) feature in SearchBlox enables intelligent data understanding by extracting entities and their relationships from indexed content. Instead of treating documents as isolated data, KG organizes information into a connected structure, improving search relevance and contextual understanding.

How Knowledge Graph Works

When Knowledge Graph is enabled for a collection, SearchBlox performs the following:

Extracts key entities such as people, organizations, locations, and concepts from documents
Identifies relationships between these entities
Structures the extracted data into a graph format
Stores this information alongside the indexed content
This allows SearchBlox to understand the meaning and context behind the data rather than relying only on keywords.

Core Components of Knowledge Graph

Entities

Entities are the main elements identified within content. These include:

People (e.g., employees, authors)
Organizations (e.g., companies, departments)
Locations (e.g., cities, countries)
Products, technologies, and concepts

Relationships

Relationships define how entities are connected. Examples include:

“works for”
“located in”
“part of”
“related to”
These connections help build meaningful associations across documents.

Triples (Data Structure)

Knowledge Graph stores data in the form of triples:

Subject → Predicate → Object

Example:

SearchBlox → uses → OpenSearch

This structure forms the foundation of the graph.

Benefits of Knowledge Graph

Enabling Knowledge Graph provides the following advantages:

Improved Search Relevance – Delivers more accurate results by understanding context
Semantic Search Support – Interprets user intent instead of matching exact keywords
Contextual Navigation – Allows users to explore related entities and topics
Enhanced Insights – Reveals hidden relationships within data

Use Case Example

Without Knowledge Graph:

A search for “Java” may return mixed and unrelated results
With Knowledge Graph:
The system distinguishes between different meanings such as programming language, location, or product
Results are refined based on context

When to Enable Knowledge Graph

Enable Knowledge Graph when:

Your data contains rich and interconnected information
You require advanced and semantic search capabilities
Understanding relationships between data is important

When to Disable Knowledge Graph

You may choose to disable Knowledge Graph when:

Your data is simple and does not contain meaningful relationships
Faster indexing is preferred over advanced processing
Relationship-based insights are not required

Performance Considerations

Enabling Knowledge Graph may increase indexing time due to entity extraction
Additional storage is used for maintaining graph relationships
Recommended for collections where data intelligence is a priority