In faceted search, users narrow down results step by step using filters built from attributes of the content – such as category, price, brand, date or author. Each facet usually shows how many results remain. The approach is familiar from online shops but is also used in libraries, intranets and document archives to make large collections manageable without exact search terms.
Information retrieval covers methods that find the documents or passages in a large body of text that match a query and rank them by relevance. Classic approaches such as BM25 weight search terms by how often and how rarely they occur; newer ones add semantic similarity. It underpins search engines, enterprise search and retrieval-augmented generation, in which a language model uses the retrieved passages as the basis for its answer.
AI search refers to search methods that use AI to grasp the intent behind a query, weight results and often generate a summarised answer with sources rather than a plain list of links. Technically, they use keyword search, vector search over embeddings or a combination, frequently followed by a language model. Examples include answer engines on the web and internal knowledge search that helps staff find what they need in manuals and policies.
Query expansion automatically adds related terms to a search query so that relevant documents using different wording are also found. Typical additions are synonyms, abbreviations, word forms or translations: a search for “cancellation” might then also find texts about “termination of contract”. This is especially useful for keyword search, but it can reduce precision if too many or unsuitable terms are added. Today, language models are often used to generate the expanded queries.
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”.
