Semantic Search Framework Using Word Embeddings for Survey Context
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Solution Overview
Problem
Existing survey data mining systems are unable to adequately understand and identify the contextual meaning of user comments, limiting their ability to filter and retrieve relevant information beyond exact keyword matches, especially in diverse language environments.
Innovation Solution
A semantic search framework that utilizes word embeddings and similarity thresholds to convert search requests into representative vectors, enabling the retrieval of contextually relevant survey data by bypassing language barriers and focusing on the actual meaning of user comments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing keyword-based search systems are used, then search speed and simplicity are maintained, but search accuracy and contextual understanding deteriorate
Solution Approach 1:
The patent transforms the search parameter from exact keyword matching to semantic similarity measurement using word embeddings. This changes the fundamental parameter of search from discrete string comparison to continuous vector space distance measurement, enabling contextual understanding while maintaining systematic processing capability.
Solution Approach 2:
The patent replaces the mechanical keyword-matching system with a semantic vector comparison system. Instead of direct string comparison operations, the system uses vector embeddings and similarity calculations to capture contextual meaning, substituting a rigid mechanical search mechanism with a more flexible semantic model.
2Loss of information
If exact match searches are performed, then search processing speed is maintained, but the ability to understand contextual meaning and language nuances deteriorates
Solution Approach 1:
The patent performs preliminary action by pre-computing word embeddings and storing them in a vector database before search operations. This advance preparation converts text to semantic vectors beforehand, so that during actual search, the system only needs to perform vector comparison rather than full semantic analysis, reducing real-time processing time while preserving contextual meaning.
3Adaptability or versatility
If traditional search functionality is used, then system simplicity is maintained, but the ability to retrieve contextually relevant data across different language expressions deteriorates
Solution Approach 1:
The patent implements a universal search framework that handles multiple language expressions and contextual variations through a single semantic search mechanism. The word embedding model provides multi-functionality by representing different words and phrases with similar meanings as vectors close in space, allowing the same search infrastructure to understand diverse language expressions without requiring separate processing paths.
Data Source
AI summary
Disclosed are systems and methods for a semantic search framework that operates to provide a robust search feature of database hosted data. The disclosed framework (or tool) improves and expands how searches can be configured and executed. In some embodiments, the disclosed framework is configured to receive a search request for a term or phrase, contextualize it to a generally understood theme that is not hindered by language barriers, and leverage it in a manner that is able to retrieve the most relevant and accurate results. The disclosed framework can filter search requests in a manner that both expands its breadth while honing in on what is actually being requested. The disclosed framework can be embodied as computerized systems and methods that can topically search for content based on a query string (e.g., term or phrase), and output a results set that embodies the theme of a survey's feedback.