Query reformulation in semantic search involves modifying user queries to improve relevance and retrieval accuracy. Common techniques include synonym expansion, query expansion with related terms, and neural-based rewriting. These methods help bridge the gap between how users phrase queries and how concepts are represented in the data, addressing ambiguity or vocabulary mismatches.
One foundational approach is synonym-based expansion, which uses predefined lists or knowledge bases like WordNet to add alternative terms to a query. For example, a search for “automobile” might expand to include “car” or “vehicle.” Modern systems often replace manual synonym lists with embeddings from models like Word2Vec or BERT, which can automatically detect semantically related terms. For instance, using BERT’s contextual embeddings, a query like “ways to stay fit” might be expanded to include “exercise routines” or “healthy habits.” Another method is query relaxation, where parts of the query are removed or simplified if initial results are sparse. For example, if a user searches for “affordable electric cars with 400-mile range,” the system might drop the mileage constraint if no matches are found, prioritizing “affordable electric cars” instead.
Another category involves user feedback and session context. Systems analyze past interactions, such as clicked results or reformulated queries in the same session, to adjust the current query. For example, if a user initially searches for “Python tuples” and later refines it to “immutable sequences in Python,” the system might learn to associate “tuples” with “immutable sequences” for future queries. Neural techniques like transformer-based rewriters (e.g., T5 or GPT) take this further by generating entirely new paraphrases. For instance, the query “best laptop for coding” might be rewritten as “top-rated portable computers for software development.” Hybrid approaches combine rules (like boosting specific keywords) with neural models to balance precision and flexibility.
Finally, hybrid systems often blend multiple techniques. A pipeline might first expand the query using synonyms, then apply a neural rewriter, and finally adjust weights based on domain-specific rules (e.g., prioritizing “GPU” in a query about gaming laptops). Tools like Elasticsearch’s synonyms API or frameworks like Hugging Face’s transformers library enable developers to implement these strategies. For example, a medical search system might use UMLS-based synonym expansion alongside a BERT model fine-tuned on clinical text to handle both technical jargon and layman terms. The key is to test and iterate—measuring metrics like recall@k or user engagement—to determine which combination works best for a specific dataset or use case.