What is a Knowledge Graph?

What does a Knowledge Graph mean?

A Knowledge Graph is a data model that organizes information into nodes (entities) and edges (relationships).
Unlike traditional databases that store data in rows and tables, a knowledge graph connects data based on meaning and relationships — showing how things are linked in the real world.

In industrial contexts, knowledge graphs connect equipment, suppliers, parts, and documentation to create a unified data landscape across ERP, EAM, and engineering systems.

How Knowledge Graphs differ from traditional databases

Traditional relational databases store isolated data points, while knowledge graphs make contextual connections — allowing systems to “understand” how data fits together, enabling advanced reasoning and automation.

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How Knowledge Graphs work

Knowledge graphs use semantic models, ontologies, and reference data libraries (RDLs) (like CFIHOS or ISO 8000) to describe and connect information consistently.
Each entity (like a pump, document, or supplier) is represented as a node with attributes and linked to related entities through defined relationships.

Core components of a Knowledge Graph

Entities – Represent real-world objects (e.g., pump, asset, material).
Relationships – Describe how entities are connected (e.g., “pump uses motor”).
Ontologies – Provide structure and standard definitions for terms.
Linked Data – Enables systems to exchange information seamlessly using shared identifiers.

This structure forms the foundation for semantic search, AI reasoning, and data interoperability across enterprise systems.

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Benefits of Knowledge Graphs

Improved Data Integration

Knowledge graphs unify data across departments and systems — breaking down silos between engineering, procurement, and operations.‍

Enhanced Decision-Making

Connected, contextualized data enables advanced analytics, AI recommendations, and predictive insights.‍

Foundation for Digital Twins

By linking assets, equipment, and processes, knowledge graphs create the semantic backbone of digital twins, ensuring real-time data accuracy and synchronization across operations.

How Sharecat supports Knowledge Graph adoption

Sharecat Data Services enables organizations to implement knowledge graph principles by:

Structuring data with semantic models aligned to standards such as CFIHOS, ISO 8000, and E-Class.
Building linked data networks that connect spare parts, assets, and suppliers.
Integrating with ERP and EAM systems like SAP, Maximo, and IFS to enhance interoperability.

By connecting structured and unstructured data sources, Sharecat helps clients build enterprise-wide knowledge graphs that support smarter maintenance, planning, and engineering decisions.

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Glossary