What is semantic search? / What is semantic search?

What is semantic search?

Semantic search is a search method that focuses on the meaning and context of words, not just the exact words themselves. Unlike traditional search engines that look for keyword matches, semantic search tries to understand what the searcher really wants to know. It analyzes relationships between words, the intent of the query and the overall content of documents to provide the most relevant and meaningful results.

Imagine you ask a friend: ” Who won the big tennis tournament in 2023? ” Your friend knows that you are asking about Wimbledon, Novak Djokovic and specific details. Semantic search aims to understand your query in the same way.

How does semantic search work?

Comprehensive understanding of requests

Semantic search engines analyze a query to understand what is actually being asked. They identify entities (people, places, things), recognize relationships and determine the search intent. This helps them provide answers instead of just web pages with matching keywords.

Example: If someone searches for ” best laptops for students “, the semantic search understands that they are looking for affordable, portable and reliable computers, not the exact phrase.

Analysis at document level

Instead of just searching for keywords, semantic search engines evaluate the overall content and meaning of documents. They check whether a document answers questions, provides explanations or discusses related topics. This helps them to evaluate content that really addresses the query, not just pages with many keyword occurrences.

Expansion of keywords

Semantic search combines keywords with related ideas, synonyms and concepts. A search for ” car ” also returns results for ” vehicle “, ” automobile ” and specific brands. This extension helps to provide more comprehensive and relevant results.

Ranking based on importance

Semantic search engines evaluate results based on how well they address the query in terms of content. They prefer pages that answer questions, provide explanations or lead content-related discussions. Factors such as user engagement, content quality and expertise play an important role.

Important components of the semantic search

Understanding at entity level

Semantic search identifies and understands specific entities within a query and a document. Entities are specific persons, places, organizations or things.

Examples:

• “Barack Obama” (person)
• “Mount Everest” (location)
• “Google” (Organization)

By understanding entities, semantic search can recognize relationships and provide results that go beyond simple keyword matches.

Word relationships and vectors

Words have relationships to each other that can be captured by machine learning models such as Word2Vec or GloVe. These models represent words as vectors in a mathematical space, making semantic similarity measurable.

Examples of word relationships:

• Synonyms: quick, fast, swift
• Antonyms: hot, cold
• Contextual similarity: doctor – hospital, teacher – school

Semantic search engines use these relationships to rank results based on meaning and relevance.

Important examples and applications

1. Example 1: E-commerce search: A user searches for ” waterproof hiking boots ” on a retail website. A semantic search engine understands that she is looking for footwear that is suitable for outdoor activities and protected against water. It displays products labeled as ” waterproof “, ” designed for trail running ” or ” outdoor footwear “, even if the exact keyword is not present.
2. Example 2: Health information retrieval: A researcher searches for ” treatments for malignant melanoma ” in a medical database. The semantic search recognizes that ” malignant ” refers to cancer and ” melanoma ” is a specific type of cancer. It retrieves studies on ” cancer treatments “, ” immunotherapy for melanoma ” and ” oncology therapies “.

Why is semantic search important?

Better user experience

Semantic search delivers more relevant and meaningful results, reducing frustration and the need to rephrase queries. Users find what they are looking for faster, resulting in a smoother search journey.

Example: A student searching for ” causes of the French Revolution ” will get content that explains the underlying social, economic and political factors, not just pages with the phrase.

Advantages for SEO

For marketers and website owners, understanding semantics means creating content for humans, not just algorithms. Ranking for broad, semantically related terms brings targeted traffic that converts. High quality content that appeals to intent will perform well in semantic search.

Progress in AI and innovation

Semantic search drives AI by teaching machines to understand language and meaning. This enables smarter applications, from virtual assistants to personalized recommendations.

Comparison: Lexical vs. semantic search

Lexical search: Matching words

Lexical search, also known as keyword search, finds results based on exact word matches. It is effective if the searcher knows the correct keywords, but fails if there are discrepancies between the query and the terminology of the results.
Example: A search for ” affordable laptops ” will miss pages that say ” low cost notebooks ” because there is no exact word match.

Semantic search: Matching meaning

Semantic search finds results based on meaning, context and relationships between words. It can recognize synonyms, related concepts and implied meaning.
Example: The same search for ” affordable laptops ” retrieves results for ” low cost laptops “, ” best cheap computers ” and ” student laptop deals “.

Comparison table

Frequently asked questions about semantic search

What is semantic vs. syntactic search?

The syntactic search is based on the literal structure and order of the words in a query. It searches for exact phrases or word matches without taking the meaning into account. The semantic search ignores the word order and concentrates instead on the meaning and relationships between words.

What is a semantic search example?

A user searches for ” ways to reduce carbon footprint “. A semantic search engine returns results about ” emission reduction “, ” sustainable practices ” and ” green energy “, even if the exact keyword is not present.

Related terms and concepts

Semantic retrieval

Semantic retrieval refers to finding information based on meaning and context, not just word matches. It is used in knowledge databases, internal search engines and specialized databases.

Semantic indexing

Semantic indexing creates indexes that contain information about entities, relationships and semantic vectors. These indexes allow search engines to quickly retrieve relevant results based on meaning.

Conclusion

Semantic search is changing the way we find and understand information. By understanding meaning, context and relationships, it delivers more relevant and meaningful results. As technology evolves, semantic search will play a central role in shaping the future of information access.

Important findings

• Semantic search focuses on meaning and context, not just word matches.
• It improves relevance, ranking and user satisfaction.
• Core technologies include NLP, knowledge graphs and embeddings.
• Applications range from e-commerce to healthcare and research.

Optimize your search experience

Consider the integration of semantic search capabilities when creating intranet search solutions or AI agents. Vectice’s industry search module provides pre-trained models tailored to specific industries, ensuring precision and relevance.

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