What is a Knowledge Graph?

A Knowledge Graph is a structured representation of information that connects data points through relationships, enabling artificial intelligence systems to understand context and meaning. Instead of storing data in isolated tables, a knowledge graph organises it as nodes (entities) and edges (relationships), forming a network of interconnected insights.

Knowledge graphs allow machine learning models and large language models to reason about complex information, improving search relevance, data discovery, and decision-making across enterprise systems.

How a knowledge graph works

• Entities: Represent people, places, concepts, or objects (e.g. “customer,” “product,” “transaction”).
• Relationships: Define how entities are connected (e.g. “purchased,” “belongs to,” “located in”).
• Attributes: Store key details about each entity, such as dates, identifiers, or metrics.
• Graph schema: Defines the structure and rules for how entities and relationships are organised.
• Querying: Uses graph query languages like SPARQL or Cypher to explore and extract insights.

Key benefits of knowledge graphs

• Contextual understanding: Connects data meaningfully, allowing AI to interpret relationships rather than isolated facts.
• Data integration: Unifies structured and unstructured data from multiple sources into a single connected framework.
• Explainability: Improves model interpretability by showing how AI decisions are derived from linked data.
• Scalability: Supports dynamic updates as new data is added, keeping relationships current and relevant.
• Enhanced search: Powers semantic search engines that understand user intent and contextual meaning.

Applications of knowledge graphs

• Enterprise data management: Connects siloed business systems for unified analytics and reporting.
• Search and recommendation: Improves relevance in search engines, eCommerce, and content discovery.
• Generative AI grounding: Enhances accuracy in generative AI and Retrieval-Augmented Generation (RAG) by providing verifiable knowledge sources.
• Fraud detection: Identifies hidden relationships between entities in data governance and compliance contexts.
• Customer 360 insights: Builds comprehensive views of customers across multiple touchpoints and systems.

Challenges in building knowledge graphs

• Data quality: Requires accurate, well-structured, and maintained datasets.
• Complexity: Designing an effective schema and maintaining relationships can be resource-intensive.
• Integration: Combining diverse data formats from different sources demands strong MLOps and data engineering pipelines.
• Scalability: Large graphs need specialised storage and querying solutions for performance at scale.

The future of knowledge graphs in AI

Knowledge graphs are becoming fundamental to intelligent systems that require contextual understanding and reasoning. When combined with large language models and vector databases, they create hybrid AI architectures capable of both learning from data and referencing structured knowledge for factual accuracy.

Learn more: At Shipshape Data, we help organisations design and deploy knowledge graph frameworks that integrate seamlessly with data governance, analytics, and responsible AI strategies — ensuring data is connected, explainable, and enterprise-ready.

Book a discovery call to explore how knowledge graphs can transform your data into a connected intelligence ecosystem.