Information retrieval (IR) systems handle ambiguous queries by employing techniques to interpret user intent and refine results. Ambiguity arises when a query term has multiple meanings (e.g., “jaguar” as an animal or a car brand) or lacks context. To resolve this, IR systems use methods like query expansion, contextual analysis, and user feedback. These approaches help narrow down possible interpretations and improve the relevance of returned results.
One common strategy is query expansion, where the system adds related terms or synonyms to the original query. For example, if a user searches for “apple,” the system might expand the query to include terms like “fruit” or “iPhone” based on common associations. Contextual clues, such as the user’s search history, location, or previously viewed pages, also play a role. If a user frequently searches for tech products, “apple” might prioritize results about the company. Systems may also analyze surrounding terms in the query—like “apple pie recipe” versus "apple stock price"—to infer the intended meaning. Additionally, some IR systems use pre-built knowledge graphs (e.g., Wikidata) to map entities and their relationships, distinguishing between “Michael Jordan” the athlete and “Michael Jordan” the academic.
Machine learning models further enhance ambiguity resolution. Techniques like word embeddings (e.g., Word2Vec) capture semantic relationships between terms, helping the system identify which meaning aligns with other words in the query. For instance, “bank” paired with “river” might trigger results about geography, while “bank” with “loan” focuses on finance. If ambiguity persists, systems may prompt users to clarify, such as offering disambiguation pages with multiple options. Feedback loops—like tracking which results users click—also refine future queries. Together, these methods balance automation and user input to deliver relevant results despite ambiguity.