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Semantic search

Semantic search denotes search with meaning, as distinguished from lexical search where the search engine looks for literal matches of the query words or variants of them, without understanding the overall meaning of the query. Semantic search is an approach to information retrieval that seeks to improve search accuracy by understanding the searcher's intent and the contextual meaning of terms as they appear in the searchable dataspace, whether on the Web or within a closed system, to generate more relevant results. Modern semantic search systems often use vector embeddings to represent words, phrases, or documents as numerical vectors, allowing the retrieval engine to measure similarity based on meaning rather than exact keyword matches.

Models and tools
Tools like Google's Knowledge Graph provide structured relationships between entities to enrich query interpretation. Models like BERT and Sentence-BERT convert words or sentences into dense vectors for similarity comparison. Semantic ontologies like Web Ontology Language, Resource Description Framework, and Schema.org organize concepts and relationships, allowing systems to infer related terms and deeper meanings. Hybrid search models combine lexical retrieval (e.g., BM25) with semantic ranking using pretrained transformer models for optimal performance. ==See also==
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