Semantic search
Work more efficiently with search that understands your search intent for more relevant, precise and timely results.
Why it matters
Semantic search improves the accuracy of search results by understanding the intent and context behind a user's query. Unlike traditional search engines that rely solely on keywords, semantic search takes into account the meaning of words, phrases, and the relationships between them, offering a far more precise and user-friendly experience.
How it works
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Extract meaning and intent
When a user enters a search query, the semantic search system extracts that user’s meaning and intent by analyzing the words and phrases used in the query, as well as synonyms, related terms and relationships between words. For example, if a user searches for "Taiwan Strait," the search engine might also consider terms like "Taiwan independence," "One China policy," and "Cross-Strait relations" as relevant to the query.
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Achieve greater relevance
Semantic search then breaks each word or piece of text down into chunks which get mapped in the "semantic space." The semantic space is a mathematical framework where words with similar meanings or semantic relationships are represented as nearby vectors in a multidimensional space. Words with different meanings or unrelated semantic concepts are vectors that are represented farther apart. The distance between points in the semantic space reflects the degree of semantic similarity between the corresponding words or pieces of text. The closer they are in the semantic space, the higher the relevancy.
Semantic search systems utilize machine learning models to continuously refine and update the semantic space based on user interactions and feedback. This allows the system to adapt and improve its understanding of language nuances and user intent over time, resulting in more accurate and relevant search results. Ultimately, semantic search revolutionizes information retrieval by enabling a deeper understanding of language semantics and providing users with more precise and contextually relevant information.
Why choose Primer
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Knowledge base
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Resources
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Discover how NLP is the key to effectively searching and analyzing data at scale and in real-time.
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Open Source Intelligence (OSINT) has emerged as a formidable tool to understand the rapidly evolving information landscape to gain insights, mitigate risks, and make informed decisions.
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Optimize AI costs with Primer’s BabyBear, a framework for cascading models for natural language processing (NLP) tasks to minimize GPU cost.
