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Glossary

Knowledge Engineering

What is Knowledge Engineering?

Knowledge engineering covers capturing, organising and preparing expert knowledge so that IT systems can use it. Originally it focused on rules for expert systems; today the priority is often to document scattered knowledge from manuals, process descriptions and people's heads so that AI agents can answer reliably from it. This involves clear structures, up-to-date sources, defined responsibilities and closing identified gaps.

How KI.ordo helps

KI.ordo combines organisational consulting with the manual assistant and the knowledge feature, which also keeps a list of unanswered questions as a pointer to knowledge gaps.

Learn more →: Knowledge Engineering

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Topics

  • Knowledge management
  • Consulting
  • Documentation

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Related terms

Ontology

In computer science, an ontology is a formal model of a subject area that clearly defines concepts, their properties and the relationships between them. This allows people and machines to interpret data consistently and draw conclusions from it. Ontologies underpin many knowledge graphs, semantic search systems and data integration projects. Example: a product ontology states that an “e-bike” is a “bicycle” and has a “battery”.

  • knowledge modelling
  • semantics
  • +1

Expert system

An expert system is a computer program that stores the know-how of human specialists as rules and facts and draws conclusions from them. It typically consists of a knowledge base and an inference engine that applies the rules. Expert systems shaped AI in the 1970s and 1980s, for example in medical diagnosis or configuring technical equipment. Their ideas live on today in rule-based checks and knowledge graphs.

  • Rule-based AI
  • Knowledge base
  • +1

Question answering

Question answering (QA) is a field of natural language processing in which a system answers questions posed in everyday language directly, rather than returning a list of search results. Modern systems look for relevant passages in a knowledge base and compose an answer from them, ideally with a source reference. Typical uses are customer service, internal knowledge bases and manuals – for instance asking the staff handbook ‘How many days’ leave do I get in my first year?’

  • NLP
  • Knowledge management
  • +1
Template

Retrieval-augmented generation (RAG)

Retrieval-augmented generation combines search with text generation: before answering, the system searches a knowledge collection – such as manuals, policies or FAQs – for relevant passages and passes them to the language model as context. The answer is therefore based on current, company-specific content rather than only on the model's training knowledge. This reduces invented statements, makes sources traceable and allows updates without retraining.

  • RAG
  • knowledge search
  • +1