Semantic Index Search for Context-Rich Interactive Data
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Solution Overview
Problem
Conventional keyword-based search methods struggle to accurately capture the semantic complexity of interactive data, particularly in colloquial and diverse conversational styles, and existing neural network-based models face challenges in context recognition and require extensive labeling and retraining, leading to reduced user experience and search delays.
Innovation Solution
A method and apparatus that convert interactive data messages into a sentence structure with a single meaning (STID) using a semantic index, generating embedding vectors for similarity comparison, allowing for high-performance search without extensive labeling or retraining, by utilizing large-scale language models and embedding models like BERT or sentence transformers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If keyword-based search methods are used, then search speed is maintained, but search accuracy deteriorates due to inability to capture semantic complexity
Solution Approach 1:
The patent introduces a semantic index as an intermediary structure between keyword-based search and full semantic analysis. The semantic index pre-processes and structures interactive data into meaningful segments (user input, assistant response, key information), enabling accurate semantic matching without requiring complex neural network models during actual search operations.
Solution Approach 2:
The patent applies preliminary action by pre-processing interactive data into semantic indices before search operations. The semantic index structures data in advance with clear semantic relationships, so that during search, the system can directly compare and match without performing complex real-time semantic analysis, thus improving accuracy while maintaining speed.
2Measurement precision
If neural network-based semantic search models are used, then context recognition improves, but search time increases due to extensive labeling and retraining requirements
Solution Approach 1:
The patent replaces expensive, time-consuming neural network models with a lighter semantic index structure that can be quickly generated and updated. The semantic index uses simple structural segmentation rather than complex neural network training, eliminating the need for extensive labeling and retraining while maintaining context recognition capability.
Solution Approach 2:
The patent segments interactive data into distinct semantic components (user input, assistant response, key information points) within the semantic index. This segmentation allows the system to capture context through structured organization rather than through complex neural network processing, achieving context recognition without the time penalty of model training and inference.
3Measurement precision
If message structures with multiple meanings are preserved, then information completeness is maintained, but search precision deteriorates due to semantic ambiguity
Solution Approach 1:
The patent applies local quality by creating specific semantic roles within the index structure (user input, assistant response, key information) that highlight different aspects of the message locally. Each segment is tagged with its specific semantic function, allowing precise search matching on particular aspects while preserving the complete message structure for reference.
Solution Approach 2:
The semantic index acts as an intermediary that resolves semantic ambiguity by structuring multi-meaning messages into distinct, labeled components. The index separates different meanings and roles within a message, allowing searches to target specific semantic aspects precisely while the original complete message remains preserved in the structured format.
Data Source
AI summary
An embodiment may provide a method of searching interactive data through a semantic index, the method including acquiring interactive data consisting of at least one message; generating a semantic index by converting the message into a sentence structure having a single meaning, wherein the sentence structure is the same for all messages; acquiring a query for a user to search for interactive data; obtaining similarity between the semantic index and the query; returning interactive data corresponding to the query based on the similarity; and providing the user with the interactive data corresponding to the query.