Semantic Search and Relational Data Integration

Relational databases remain the primary operational store for enterprise records, but users increasingly expect search behavior that tolerates unfamiliar schemas, synonymy, missing joins, and mixed textual and structured evidence. This paper presents Semantic-Relational Search Integration (SRSI), a framework for adding semantic search and lightweight data integration to existing relational sources without replacing the database engine. SRSI builds a semantic overlay consisting of schema correspondences, attribute phrases, entity links, and query-rewriting templates. Queries are interpreted through lexical, structural, and semantic signals, then compiled into ranked SQL plans and optional ontology-based access paths. The framework is intended for 2020-era deployments: relational systems, SPARQL-to-SQL rewriting, schema matching, record linkage, word and contextual text representations, and conservative human review of low-confidence mappings. A prototype study over product, publication, and service-ticket datasets shows that SRSI improves top-10 answer accuracy from 0.61 to 0.78 relative to keyword-only relational search, reduces manual mapping effort by 42%, and keeps median query latency below 1.8 seconds for the tested workloads. The results suggest that semantic search and relational integration are most practical when semantics are treated as a governed overlay over relational data rather than as a full replacement for relational storage and query processing.