teemahaun

Teemahaun is a search paradigm used in information retrieval that centers on topics or themes rather than solely on keywords. In Estonian contexts, the term translates to “topic search” and is applied in digital libraries, news portals, and enterprise search systems to help users find materials by subject matter.

It combines keyword search with topic-based indexing. Documents are annotated with topics via controlled vocabularies, subject headings, or machine-generated topic models. When a user submits a query, the system maps the query to one or more topics and retrieves documents associated with those topics. Results can be organized by topic, enabling faceted navigation and discovery of related topics.

Techniques used in teemahaun include manual indexing with taxonomies, automated topic modeling (for example, Latent Dirichlet Allocation), subject indexing, semantic expansion, and clustering. Modern implementations may use word embeddings, ontologies, and knowledge graphs to connect related topics and synonyms, improving both precision and recall.

Applications of teemahaun span academic databases, libraries, media portals, and corporate knowledge bases. Benefits include improved relevance, better handling of synonyms and polysemy, support for exploratory search, and easier discovery of related materials that share a common theme.

Challenges involve creating and maintaining topic taxonomies across domains, cross-language and multilingual issues, evolving topics over time, evaluating topic-based relevance, and balancing precision with recall. Effective teemahaun systems require ongoing curation of vocabularies, robust topic modeling, and user-centered interface design to support topic-based exploration.

See also: topic modeling, faceted search, subject indexing, controlled vocabulary, taxonomy, semantic search.