Intelligent Conversation Page Data Retrieval via Semantic Parsing
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Solution Overview
Problem
Traditional key-value store methods for file storage and retrieval are inefficient and have a high retrieval threshold, as they require exact matching of keys to retrieve corresponding content.
Innovation Solution
A method for data processing that involves parsing service processing request texts on an intelligent conversation page to extract semantic information and intention requirements, and using this information to perform targeted retrieval from a service database classified by N service types, where N is a positive integer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional key-value store method is used for file storage and retrieval, then the storage structure is simple, but the retrieval efficiency is low and the retrieval threshold is high
Solution Approach 1:
The patent segments the file storage system into multiple dimensions: traditional key-value storage structure is divided into service type classification layer and semantic retrieval layer. Files are organized by service types (e.g., maintenance, inspection, operations) with structured metadata, enabling targeted retrieval without scanning entire databases. This segmentation reduces retrieval scope and improves efficiency while maintaining simple storage access.
Solution Approach 2:
The patent introduces semantic information extraction and classification as an intermediary between user queries and database retrieval. Natural language queries are transformed into structured service type classifications and semantic keywords, which then guide the retrieval process. This intermediary layer lowers the retrieval threshold by allowing users to query in natural language rather than requiring precise key matching.
2Measurement precision
If exact matching of keys is required for retrieval, then data accuracy is ensured, but retrieval time increases and efficiency decreases
Solution Approach 1:
The patent applies preliminary action by pre-classifying and tagging files with service types, semantic keywords, and metadata during the storage phase. This pre-processing creates an indexed structure that enables rapid retrieval without requiring exhaustive key matching. The classification work is done in advance, so retrieval operations can quickly narrow down search scopes to relevant service types and keywords.
Solution Approach 2:
The patent implements local quality by creating different retrieval strategies for different service types and data characteristics. Each service type (maintenance, inspection, operations) has optimized retrieval paths based on its specific metadata structure. This allows the system to apply appropriate retrieval methods locally rather than using a single exhaustive matching approach for all data, reducing overall retrieval time while maintaining accuracy.
Data Source
AI summary
A method for data processing, a computer, and a storage medium are provided. A service processing request text sent by a first service object is obtained on an intelligent conversation page, and the service processing request text is parsed to obtain service semantic information and intention requirement information. A service database associated with the first service object is determined. A first target type indicated by the service semantic information is obtained, and a first service data set of the first target type is obtained from classified data corresponding to N service types in the service database respectively. Information retrieval is performed in the first service data set to obtain retrieval content associated with the first target component, a service result for the service processing request text is generated based on the intention requirement information and the retrieval content, and the service result is displayed on the intelligent conversation page.