AI curriculum
Course outline for Knowledge Graph Engineering
Connected data can become inconsistent without clear identities and schemas. Construct a small knowledge graph with repeatable ingestion, queries, and maintenance checks.
About Knowledge Graph Engineering
Connected data can become inconsistent without clear identities and schemas. Construct a small knowledge graph with repeatable ingestion, queries, and maintenance checks.
Knowledge Graph Engineering Course Objectives
- Define graph entities, relationships, and schema rules.
- Resolve duplicate entities using explicit criteria.
- Build queries and maintenance checks for a small graph.
Pre-requisites
- Basic data modelling and query experience.
- Familiarity with scripting or data transformation.
Lab Setup
- Computer with a local graph database or graph library.
- Use synthetic entity records and a scripting runtime; hosted database access is optional.
Detailed Course Outline
Proposed modulesModule 1: Graph structure and identity
- Knowledge graph modelling
- Graph schemas
- Graph databases
- Entity resolution
Practical outcome: Define a schema and identity rules for sample records.
Module 2: Graph construction and use
- Relationship modelling
- Querying
- Knowledge graph construction
- Knowledge graph maintenance
Practical outcome: Load relationships and validate them with queries.
Practical exercise
- Construct a small knowledge graph, resolve duplicate entities, and test a maintenance update.
How we train
Contact us for full course details, including duration, delivery options and lab requirements.
Ask about exercises, instructor feedback and the prior knowledge you need. Tool-specific courses marked provisional may become modules in a broader course.
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