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 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.
Semantic analysis examines the meaning of language – not just which words appear, but what is meant. This includes interpreting ambiguous words correctly in context (such as “bank” as a financial institution or a riverside), recognising relationships between concepts and classifying statements by content. It underpins applications such as analysing customer feedback, question answering or search systems that should also understand differently worded queries with the same meaning.
Structured data follows a fixed schema with clearly named fields, such as tables, databases or JSON objects containing details like name, price and date. Machines can read, filter and process it reliably – unlike free text or images. On websites, schema.org markup describes products, events or FAQs, for example, so that search engines and AI systems understand the content unambiguously. AI can also extract structured data from unstructured text.
A cognitive map is originally a term from psychology describing the mental representation that people or animals form of their surroundings. In management and AI, it also refers to graphical models that show concepts as nodes and their cause-and-effect relationships as weighted arrows, for instance “fuzzy cognitive maps”. Such maps help structure expert knowledge and explore scenarios, such as how price changes affect demand and stock levels.
