How Knowledge Graphs Work

Learn how a Knowledge Graph works and what sets Stardog apart

Why Knowledge Graphs?

A Knowledge Graph turns your data into machine-understandable knowledge. But what separates data from knowledge?

Real-world knowledge is:

  • situational: meaning alters depending on circumstances
  • layered: associations between concepts allow for nuanced understanding
  • changing: new discoveries instantly change meanings

These facets of knowledge represent the context that is often missing from data. When traditional data management systems attempt to capture context, they fail. These failures generate gridlock over mastering data, delay timelines when adding new data sources or properties, and cause missing data from analyses that lead to mistrust.

Knowledge Graphs, on the other hand, are purpose-built for the fluctuating nature of knowledge. They offer a more flexible foundation for digital operations by easily accepting new data, new definitions, and new requirements.

So, why Knowledge Graphs? Because being data-driven is not enough — but knowledge-driven organizations can act with full context and full confidence in their decisions.

Knowledge graph tracing information

What is a Knowledge Graph?

Old Way Approach

A different approach

The “graph” in Knowledge Graph refers to a way of organizing data that highlights relationships between data points. Graph data looks like a network of interconnected points. This is in contrast to databases like Oracle or MySQL — relational systems — where data is stored in tables. Relational systems are designed for stable business processes where the data model doesn’t change.

Less rigidity

This orientation to stability causes problematic rigidity in data integration. Data integration platforms, based on relational systems, require permanent transformation of the data to create uniformity across all entities. This creates a so-called “unified view” of data, but offers no flexibility for analysis by preventing representation of situational, layered, or changing realities.

A flexible data layer

A Knowledge Graph, with its ability to make real-world context machine-understandable, is the ideal tool for enterprise data integration. Instead of integrating data by combining tables, data is unified using graph’s ability to endlessly link concepts — without changing the underlying data. Thus data unification connects data silos and produces a flexible data layer for the enterprise.

Knowledge Graphs = Graph + AI + Virtualization

Based on Graph image

Based on graph

Graph’s flexibility alone is not enough to turn data into knowledge. Graph databases — a common use of graph — can accept new data more easily than relational databases, but functionality is limited by its single schema. But a graph database imposes one point of view of the world and requires that business logic is coded into the application directly, whereas the low-code Knowledge Graph stores logic centrally.

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Enhanced by AI

The Knowledge Graph’s Inference Engine intelligently derives new knowledge from the data and the business logic. When there are conflicting definitions — say, a Top Account in one database is called situationally a Prospect or Churn Risk — you can express in the data model that these are all “Companies” and use inference to analyze all relevant data.

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Limitless data access

A Knowledge Graph is only as powerful as the data it can access. Virtualization is key to incorporating all relevant data, enriching the Knowledge Graph. The combination of Knowledge Graphs and virtualization allows for full flexibility to evaluate dependencies across different systems and structures of data. Trace data lineage, detect correlation and causation, or perform root cause or impact analysis with ease.

Benefits of Knowledge Graphs

Launch faster, iterate easily
Stardog’s flexible data model and low-code platform mean easier updates — making you more responsive to market opportunities and customer needs.
Agile data-driven operations
Translate and automate even the most complex business processes, saving time and money by replacing laborious manual analyses and procedures.
More accurate results
With real-world context captured in your data, analytics are higher-quality and search results are more relevant. Empower employees to make better decisions with unified data.

The Stardog Difference

Access any data

Access the most popular SQL and NoSQL databases via our custom Connectors and extract info from unstructured sources with our NLP pipeline.

Advanced virtualization

Stardog’s patented virtualization accesses data remotely — whether its cloud or on-prem. No more copies of copies of data.

Best-in-class Inference Engine

Our Inference Engine was designed from the ground up to solve complex problems and offers full explainability so you can trust your results.

Unify data for insight

Put your unified data in the hands of more analysts with our BI/SQL Server, which easily connects to SQL-based BI platforms like Tableau and Power BI.

Reliable and scalable

Stardog’s enterprise-ready platform can scale up to 1 trillion triples; we’re also Kubernetes compatible and ACID-compliant.

See Knowledge Graphs in action

  • Data fabric

  • Supply chain

  • Drug discovery

  • Semantic search

  • Build your data fabric with Stardog’s Enterprise Knowledge Graph platform

    An Enterprise Knowledge Graph is the key ingredient to transforming existing data infrastructure into a data fabric. Create a flexible, reusable data layer for answering complex queries across data silos.

    See how
  • Flexible data exchange for all stakeholders

    Supply chain stakeholders from product to manufacturing to distribution create their own data silos. Stardog solves the data silo problem, unifying data from existing systems without changing the underlying data and providing end-to-end transparency.

    See how
  • Complete traceability enables deeper analysis

    Data unification uncovers correlations across disparate data sources and represents the complex inter-relationships inherent in biomedical data. Unify clinical data, scientific research, disease models, and more to accelerate drug discovery.

    See how
  • Stop sifting through irrelevant results

    Semantic search understands the user’s intent behind search terms, returning more relevant results in fewer hits. Unify data from disparate sources to build a complete repository of information; virtualization ensures the most up-to-date data is accessed.

    See how

Learn more

Download our e-guide to learn more about Knowledge Graphs and how they differ from other data management technologies.

Get the free guide