Semantic Search
By Seme Research Team · Updated May 22, 2026
Definition
Semantic Search is a search methodology that understands the intent and contextual meaning behind a query rather than just matching keywords. Unlike traditional keyword search, which relies on exact term matching and TF-IDF scoring, semantic search uses natural language understanding (NLU) models — typically transformer-based architectures like BERT or GPT — to interpret query intent and match it against semantically relevant results. In identity investigation, semantic search interprets natural language criteria such as "all researchers who worked at Tencent AI Lab between 2018 and 2022" to find people matching specific descriptions, even when no exact keyword match exists in the data.
How It Works
Semantic search in identity investigation works through three phases: intent parsing, candidate discovery, and ranking. First, the natural language query is parsed by an AI model to extract structured search parameters (role, organization, time period, location, skills). Second, the system performs parallel searches across multiple data sources — search engines, professional networks, academic databases, and social platforms — using the extracted parameters. Third, results are ranked by relevance using a scoring model that considers semantic similarity, source authority, data freshness, and cross-validation between sources. The AI may also generate follow-up queries to fill gaps in the initial results.
Example
A recruiter searches "senior NLP engineers who left Google Brain in the last 2 years and have published at NeurIPS." The semantic search engine parses this into structured criteria: role=senior NLP engineer, organization=Google Brain, departure=2024-2026, publication venue=NeurIPS. It then searches across LinkedIn, Google Scholar, Twitter, and GitHub to identify 12 matching candidates, ranked by relevance score.
Applications
- •Talent recruitment and headhunting for specialized roles
- •Competitive intelligence on team composition and talent flow
- •Academic research discovery and collaborator identification
- •Investigative journalism finding persons matching specific criteria
Key Statistics
| Metric | Value | Source |
|---|---|---|
| Query Interpretation Accuracy | 92% | Internal benchmarks |
| Average Candidates Returned | 10-50 | Seme platform data |
| Data Sources Queried | 8-12 | Seme architecture |
| Response Time | <30 seconds | Seme platform data |