Natural Language Query Segmentation for Multi-Source Answer Generation
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Solution Overview
Problem
Current search engines and natural language processors are inadequate in providing comprehensive answers to complex, human-interest driven queries that require multifaceted data interrogation from diverse information sources, as they typically rely on single or similar information sources, failing to address real-world problems effectively.
Innovation Solution
A system comprising processors and logic that receives complex queries in natural language, breaks them down into query segments associated with specific domains, and interrogates multiple information sources to generate solutions, while providing explanations of the methodologies and sources used.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If current search engines and natural language processors are used to answer queries, then the system operation is simple, but the answer comprehensiveness is insufficient because they only provide one dimensional answers from single or similar information sources
Solution Approach 1:
The system segments complex queries into multiple query segments, each associated with specific domains and information sources. This allows the system to handle comprehensive information gathering by breaking down the overall query into manageable parts that can be processed from different information sources independently.
Solution Approach 2:
The system is designed to query multiple types of information sources (databases, APIs, web sources) through a unified interface. The query processing mechanism is universal and can adapt to different information sources and domain types, enabling comprehensive answer generation without requiring separate specialized systems for each information source.
2Reliability
If multiple information sources are queried to provide comprehensive answers, then the answer quality improves, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary evaluation of the complex query to determine query segments and their associated domains before actually querying information sources. This preliminary analysis allows for optimized query formulation and source selection, reducing unnecessary processing time while maintaining comprehensive answer quality.
Solution Approach 2:
The system queries multiple information sources beyond what a single-source system would use, even if this means some redundancy. By querying more sources than strictly necessary, the system ensures comprehensive and accurate answers are obtained, accepting the time cost as a trade-off for high reliability in complex query scenarios.
3Adaptability or versatility
If complex queries are broken down into query segments and processed through multiple information sources, then the solution quality improves, but the system complexity increases
Solution Approach 1:
The system divides complex queries into query segments that can be independently processed and associated with specific domains. This segmentation enables the system to handle diverse and complex queries by treating them as collections of simpler sub-queries, improving adaptability without requiring a completely different processing architecture for each query type.
Solution Approach 2:
The system introduces an intermediary layer that translates natural language queries into structured query segments and coordinates information retrieval from multiple sources. This intermediary processing layer simplifies the overall system architecture by providing a standardized interface between query input and information source interrogation, reducing the apparent complexity while maintaining high versatility.
Data Source
AI summary
Natural language solution generating devices and methods are provided herein. Exemplary devices may execute logic via one or more processors, which are programmed to receive a complex query in natural language format, the complex query including a real-world problem that requires interrogation of a plurality of information sources in order to ascertain a response to the problem, evaluate the complex query to determine query segments, which are each included with at least one domain, wherein a domain corresponds to an information source, query the information sources to obtain responses for the query segments, and generate a natural language solution using the responses.