Semantic Search Techniques
Semantic search represents a shift from traditional keyword-based searches to searches that prioritize context, meaning, and user intent. Here are the primary techniques and mechanisms used in semantic search:
1. Vector Embeddings and Vector Search
Text, queries, and documents are converted into mathematical vectors representing their meaning in high-dimensional space.
Similarity between vectors is measured to find the most relevant results, not just exact keyword matches.cloud.google+2
Common algorithms include k-Nearest Neighbor (kNN) to match query and document vectors.
2. Natural Language Processing (NLP)
NLP algorithms analyze grammatical structure, extract entities, and understand relationships within text.
Enables search engines to comprehend query intent, even if the wording is conversational or ambiguous.wallstreetprep+2
Techniques such as part-of-speech tagging, dependency parsing, and semantic role labeling are often used.
3. Large Language Models (LLMs)
Advanced neural models (like BERT, GPT, Sentence-BERT) generate contextual embeddings for sentences, paragraphs, and documents.
These models help detect nuances and perform semantic similarity comparisons, allowing for richer information retrieval.wikipedia+1
4. Knowledge Graphs & Ontologies
Structured databases linking entities and concepts (e.g., Google’s Knowledge Graph) are used to enrich query interpretation.
Helps uncover relationships between entities and expand queries with synonyms, related concepts, and contextual information.deepgram+1
Ontologies formalize the relationships and allow queries to be evaluated for deeper semantic matches.
5. Context Incorporation
Personalization by considering user-specific factors like location, search history, and session data.
Search engines adjust results dynamically based on user context for more relevant and accurate answers.elastic+2
6. Semantic Indexing
Content is indexed based on concepts and meanings rather than just keywords.
Allows retrieval of relevant documents even when query terms do not directly match the document’s wording.wallstreetprep+1
7. Synonym Rings and Expansion
Search engines recognize synonyms and related terms, expanding queries with these useful variations.
Ensures broader and more flexible search capabilities by capturing the user’s true intent.deepgram
How Semantic Search Differs from Keyword Search
| Keyword Search (Lexical) | Semantic Search |
|---|---|
| Matches exact words/phrases | Understands intent and context |
| Ignores relationships between words | Identifies relationships (via embeddings, ontologies) |
| Limited personalization | Personalizes results by context |
| Requires precise wording | Handles vague, conversational queries |
| Fast, but often less relevant | More accurate, contextually meaningful |
Example Use Cases
E-commerce: “running shoes” matches “sneakers,” “athletic footwear,” and relevant brands.
Weather queries: “What’s the weather like in Paris next week?” returns a forecast even if phrased conversationally.
Geographical context: “football” means different things in the US vs. UK; semantic search tailors results accordingly.cloud.google+2
Key Technologies
NLP and Machine Learning models
Vector databases
Knowledge graphs and ontologies
Personalized context engines
Semantic search delivers a much richer and user-friendly search experience by understanding not just the words, but the meaning, relationships, and intent behind every query.tigerdata+5
