Data & Knowledge Intelligence

Knowledge Graphs

When useful questions require joining across records — customer to project to invoice to ticket — and someone currently assembles those joins by hand.

A knowledge graph models how entities in your organization relate, so questions crossing several records can be answered without manual assembly. The modelling is the work; the storage is comparatively easy.

Graphs earn their keep where relationships are the answer, and are overkill where a well-designed table would do.

What the work includes

  • Entity and relationship modelling grounded in real questions people ask
  • Population from source systems with identity resolution across them
  • Query surfaces suited to both applications and analysts
  • Maintenance as the underlying systems and relationships change
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// Common Questions

Questions
About This

Common questions about Knowledge Graphs.

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When useful questions require joining across records — customer to project to invoice to ticket — and some of those relationships are not represented anywhere. If the joins already exist in a relational schema, a graph adds complexity without answering anything new.

A relational database is efficient at querying relationships defined in advance. A graph is built for traversing relationships whose shape and depth are not known until the question is asked, which is what makes exploratory questions tractable.

Some from existing keys between systems, others extracted from unstructured material where a connection is stated but never recorded. The second group is usually where the value is, since those relationships are invisible to every existing query.

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