Skip to main content

Semantic Search Views

Semantic search is a technique that allows searches based on the intent and contextual meaning of search phrases, rather than just keyword matching. It understands the relationships between words and concepts to provide more relevant results.

Semantic search seeks to understand the intent and contextual meaning of search phrases, going beyond traditional keyword-based searches. It takes into account user behavior, synonyms of the search terms, and the context in which the terms are being used. As a result, semantic search provides more relevant and personalized results.

note

In 17.17, the Semantic Search Views work by finding similarities between search entities. Upcoming releases will add more nuanced and expanded functionality.

Semantic search brings a paradigm shift to the search arena, allowing for a richer, more intuitive, and relevant search experience. Its ability to search based on similarity and context rather than just exact matches provides developers with tools to create more dynamic, user-centric applications. Here are some examples:

  • Personalized User Experiences:

    • Traditional searches often yield generic results, leading to less engaging user experiences.
    • Semantic search understands user behavior, preferences, and interactions, enabling personalized recommendations that enhance user engagement and satisfaction.

  • Efficient Multimedia Content Search:

    • Text-based searches are limited in handling multimedia content like images, audio, or videos.
    • Semantic search converts multimedia content into meaningful representations, facilitating fast and accurate searches for even ambiguous or abstract queries.

  • Robust Anomaly Detection:

    • Identifying anomalies in large datasets, such as fraudulent transactions, is challenging.
    • Semantic search can pinpoint anomalies by contrasting them against normal patterns, enhancing detection accuracy.

  • Real-time Data Analysis:

    • Traditional search mechanisms often struggle with real-time data, leading to delayed analysis.
    • Semantic search engines efficiently process real-time data streams, providing instant insights and analysis.

  • Enhanced Textual Analysis:

    • Keyword-based approaches for textual analysis can miss nuanced meanings and deeper semantic relationships.
    • Semantic search captures these complex relationships, allowing for a more thorough analysis of content, from sentiment to thematic depth.

  • Dynamic Product Recommendations in E-commerce:

    • Semantic search enables e-commerce platforms to analyze intricate product details, aligning suggestions closely with user interests.
    • For example, a search for "vintage leather boots" would yield recommendations that are tailored to the user's past browsing history and preferences.

  • Improved Ad Targeting:

    • Digital marketing platforms benefit from semantic search by analyzing user behavior, past purchases, and browsing history.
    • This results in highly relevant ad placements, like eco-friendly products for users interested in "sustainable living," thanks to the nuanced understanding semantic search provides.

Examples of Semantic Search Use Cases​

  • Enhanced News Article Discovery: Users searching for "recent technological advancements" receive the latest articles on cutting-edge technology developments.
  • Intuitive Recipe Suggestions: Searches for "easy vegan dinner recipes" yield simple, plant-based dinner options.
  • Localized Event Information: Queries like "weekend events near me" result in current local events, concerts, or activities.
  • Tailored Fashion Product Searches: A search for "affordable summer dresses" presents economically priced, season-appropriate dresses.
  • Advanced Academic Research Assistance: Researchers querying "latest studies on renewable energy" get recent scholarly articles and papers on renewable energy.
  • Customized Movie Recommendations: Users looking for "award-winning foreign films" find a curated list of internationally acclaimed movies.
  • Precise Medical Information Retrieval: Medical students searching for "case studies on rare diseases" access specific research papers related to rare conditions.
  • Real Estate Listings Matching: Searches for "three-bedroom homes in downtown Chicago" display listings for such houses in that specific area.
  • E-commerce Product Precision: E-commerce sites interpret searches like "red sneakers for running" and return relevant results, beyond exact product title matches.
  • Voice Assistant Accuracy: Voice assistants answer spoken questions such as "what's the weather tomorrow in Paris?" with direct responses.
  • Corporate Knowledge Base Efficiency: Searching for "refund policy" in a corporate knowledge base retrieves the specific document outlining the refund policy.

Semantic Search Queries​

Semantic search view features are integrated into C8QL as a set of C8QL functions.

For more information, refer to Semantic Search Queries.