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Search & Retrieval

Query Understanding

Query understanding is how a search system interprets a query’s meaning (entities, intent, ambiguity) before retrieving and ranking results.

Also known as: Query interpretation, Query parsing, Entity recognition

Definition

Query understanding is the set of techniques a search system uses to interpret what a query means. It covers parsing, language detection, spelling normalization, entity recognition, and disambiguation so retrieval can match meaning, not just keywords.

Why it matters

  • Less ambiguity: “CIR 92” and “CIR 1992” should resolve to the same concept.
  • Better matching: entity-aware search finds the right documents even with short queries.
  • Safer expansion: understanding prevents “helpful” expansions that change intent.
  • Better UX: enables suggestions, filters, and result grouping.

How it works

Query -> normalize -> extract entities -> resolve ambiguity -> build retrieval query

The output is typically a structured representation (tokens, entities, intent, filters) used by the ranking pipeline.

Practical example

A user types “VAT deadline Belgium Q4”. Good query understanding detects the jurisdiction (Belgium), the topic (VAT), and the period (Q4) and boosts authoritative guidance for the correct filing period.

Common questions

Q: Is query understanding only for semantic/vector search?

A: No. Even classic full-text systems benefit from normalization, synonyms, and entity extraction.

Q: How do you measure improvements?

A: Use search analytics to track zero-result rate, reformulations, click satisfaction, and offline relevance judgments.

References

Manning, Raghavan & Schütze (2008), Introduction to Information Retrieval.