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.^[1] Semantic search 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.

Some authors regard semantic search as a set of techniques for retrieving knowledge from richly structured data sources like ontologies and XML as found on the Semantic Web.^[2] Such technologies enable the formal articulation of domain knowledge at a high level of expressiveness and could enable the user to specify their intent in more detail at query time.^[3] The articulation enhances content relevance and depth by including specific places, people, or concepts relevant to the query.^[4]

Knowledge Graphs

Tools like Google’s Knowledge Graph provide structured relationships between entities to enrich query interpretation.^[5]

Vector Representations (Embeddings)

Models like BERT or Sentence-BERT convert words or sentences into dense vectors for similarity comparison.^[6]

Ontology-Based Search

Semantic ontologies like OWL, RDF, and Schema.org organize concepts and relationships, allowing systems to infer related terms and deeper meanings.^[7]

Hybrid Search Models

Combines lexical retrieval (e.g., BM25) with semantic ranking using pretrained transformer models for optimal performance.^[8]

Applications

• Web Search: Google and Bing integrate semantic models into their ranking algorithms.
• E-commerce: Intent-based product searches improve conversion and discovery.^[9]
• Enterprise Search: Corporate systems use it for document retrieval, customer support, and knowledge management.^[10]
• Healthcare and Legal Research: Facilitates retrieval of case law, research articles, and clinical data.^[11][12]

Challenges

• Ambiguity and Polysemy (e.g., "jaguar" as an animal or a car brand)
• Bias in Training Data^[13]
• Computational Costs of deep semantic models^[14]
• Multilingual Performance^[15]

Future Directions

• Conversational Search and voice interfaces
• Multimodal Search: Incorporating video, image, and text together^[16]
• Explainability and ethical transparency in semantic systems

See also

• List of search engines
• Semantic web
• Semantic unification
• Resource Description Framework
• Natural language search engine
• Semantic query
• Vector database
• Word embeddings