Data and knowledge

Knowledge graphs and semantic systems

Knowledge graphs and semantic systems work is about describing entities, their relationships and the rules that bind them, so an AI product can retrieve facts it can trace and trust. It gives retrieval a structure that similarity search alone cannot supply.

Teams building retrieval and agent products keep hitting the same wall. Answers sound fluent but cannot be traced, and similar text is not the same as the same thing. Explicit entities, relations and constraints fix that. Hiring is tricky because the skills sit across knowledge engineering, information science, databases and ontology design. Few people have shipped this inside a modern AI stack, and many strong candidates describe their work in older vocabulary.

Hiring now 3Emerging 1

Roles in this family

4 roles

Hiring nowKnowledge Graph EngineerA Knowledge Graph Engineer builds linked records of the people, products and things a business cares about, each traced to its source. It gives AI…Hiring nowOntology EngineerAn Ontology Engineer defines the concepts, relationships and rules that describe a business domain, so retrieval and AI systems return answers that…Hiring nowAI Knowledge ArchitectAn AI Knowledge Architect designs how an application organises, governs and updates what it knows. That structure decides whether retrieval is…EmergingNeuro-Symbolic Knowledge EngineerA neuro-symbolic knowledge engineer connects structured knowledge, such as ontologies and graphs, to learned models. The aim is answers that stay…

Where the people come from

Knowledge engineering, information science, databases or ontology design

Not to be confused with. Vector similarity does not replace explicit knowledge semantics.

Core skills

  • SQL
  • Information retrieval
  • Knowledge representation
  • Data quality and provenance

Where it is used

  • Enterprise software and developer tools
  • Legal services and intellectual property
  • Government and public administration

More in Data and knowledge

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