What is Semantic Search?

Semantic search differs from traditional keyword search by using Natural Language Processing (NLP) to understand the intent and contextual meaning of words. In recruitment, it matches candidates to job descriptions based on the relationship between skills and experiences, rather than exact keyword matches, enabling the discovery of candidates with transferable skills who might use different terminology.

Semantic search in recruitment uses NLP and vector embeddings to match candidates and jobs based on meaning rather than exact keyword overlap. This allows hiring platforms to recognize that terms like “JavaScript” and “Node.js,” or “project lead” and “program manager,” can represent equivalent or highly related experience, even when the exact words differ.

By converting both job descriptions and resumes into high-dimensional vectors and comparing them via cosine similarity, semantic search surfaces candidates whose skills and experiences are contextually aligned with the role. This significantly reduces false negatives that occur in traditional keyword-based ATS filters, where research suggests up to 40% of qualified applicants are rejected before human review.

In platforms like TuraHire, semantic search processes both structured data (titles, skills) and unstructured narrative text (work summaries, project descriptions), ensuring that all parts of a candidate profile contribute to the match score. The process is mathematically precise—based on geometric similarity in a learned language space—rather than vague “AI guessing.”

Semantic search does not replace recruiters; it improves the quality of the shortlist by expanding the effective talent pool and enabling skills-based matching. Its effectiveness ultimately depends on the quality of the underlying embedding model that represents language and skills.