Semantic Mapping for LLM Query Precision Across Data Tables
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Solution Overview
Problem
Existing analytic platforms lack the ability to leverage semantic mapping data effectively for natural language queries, limiting the precision and depth of responses to user queries.
Innovation Solution
A system utilizing a large language model (LLM) trained on semantic mapping information to process queries and generate natural language responses based on relationships between data elements stored in data tables, enhancing the understanding and response capabilities of analytic platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional analytic platforms process natural language queries without semantic mapping integration, then system complexity remains low, but response precision and analytical depth deteriorate
Solution Approach 1:
The system segments the complex task of natural language query processing into distinct components: semantic mapping data generation, semantic relationship extraction, and LLM-based query processing. This segmentation allows each component to be optimized independently while maintaining overall system manageability and precision.
Solution Approach 2:
The system performs preliminary action by pre-generating semantic mapping data and storing semantic relationships between data elements before query processing occurs. This pre-computed semantic knowledge base enables the LLM to quickly access meaningful relationships during query processing, improving response precision without adding complexity to the real-time query handling.
2Adaptability or versatility
If semantic mapping data is integrated into LLM training, then analytical capabilities improve, but training data requirements and processing complexity increase
Solution Approach 1:
The system extracts only the essential semantic mapping data and semantic relationships from the broader data ecosystem, separating these critical training components from unnecessary data. This extraction approach provides the LLM with focused semantic knowledge that enhances analytical capabilities while avoiding the burden of processing excessive training data.
Solution Approach 2:
The system applies local quality by ensuring the semantic mapping training data is highly relevant and precise for the specific domain context. Rather than using generic or uniformly distributed training data, the semantic mapping information is tailored to the local characteristics of the data warehouse schema and business domain, maximizing training efficiency and analytical versatility.
3Measurement precision
If semantic mapping information is used to train LLM, then natural language understanding improves, but data processing time increases
Solution Approach 1:
The system performs preliminary action by pre-computing and storing semantic mapping information, including semantic relationships between data elements, before LLM training and query processing. This pre-prepared semantic knowledge structure enables the LLM to quickly understand natural language queries without requiring extensive real-time data processing, thus improving understanding while minimizing time loss.
Data Source
AI summary
A system includes a storage device and at least one processor in communication with the storage device. The at least one processor receives a query associated with a plurality of data tables stored in the storage device. The at least one processor processes the query using a large language model (“LLM”) trained on semantic mapping information that describes relationships between data elements stored within the plurality of tables. The at least one processor generates, with the LLM, a natural language response to the query based on semantic mapping data generated from the data elements stored withing the tables. A method and computer-readable medium are also disclosed.