Entity Page Caching for Natural Language Query Processing
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Solution Overview
Problem
Natural language processing systems face inefficiencies in processing and resource utilization due to the need for significant processing resources for disambiguation and repeated generation of similar answers to multiple queries, leading to wasted resources and suboptimal performance.
Innovation Solution
A method that pre-generates and stores answers to natural language queries, using a caching system to determine if a query has a stored answer, and if not, generates a dynamic answer, which can also vary related queries to store additional potential answers, thereby reducing redundant processing and improving resource management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system performs word sense disambiguation and generates answers to natural language queries, then the system can provide accurate and relevant answers to user queries, but the system consumes significant processing resources and may waste resources repeatedly generating the same answer
Solution Approach 1:
The system pre-computes and stores answers to natural language queries in a database before they are actually needed. When a query arrives, the system checks the database first and returns pre-computed answers if available, avoiding redundant processing. This preliminary action of pre-computing answers resolves the contradiction by maintaining answer accuracy while reducing processing resource consumption through caching.
2Adaptability or versatility
If the system generates dynamic answers to queries, then the system can adapt to different user needs and provide relevant information, but the system increases processing time and resource utilization
Solution Approach 1:
The system pre-computes answers for queries and stores them in a database, so when actual queries arrive, the system can retrieve pre-computed answers immediately without performing complex dynamic generation. This maintains adaptability by having pre-computed answers ready while significantly reducing processing time.
Solution Approach 2:
The system creates a cached copy of pre-computed answers in a database, allowing rapid retrieval without re-generating. This copying approach maintains the adaptability of having query-specific answers while eliminating the time-consuming dynamic generation process for repeated queries.
3Measurement precision
If the system uses comprehensive world knowledge and ontologies for disambiguation, then the system can accurately interpret complex queries, but the system requires significant processing resources and complex data structures
Solution Approach 1:
The system pre-processes and stores the results of complex disambiguation operations in a database, breaking down the complex task into pre-computed components. This maintains query interpretation accuracy by using comprehensive world knowledge while reducing system complexity during actual query processing through database lookup.
Solution Approach 2:
The system extracts and stores pre-computed disambiguation results and answers in a database, separating the complex knowledge base from the query processing path. This allows accurate query interpretation using comprehensive ontologies while simplifying the actual query handling to a straightforward database search.
Data Source
AI summary
An answer engine determines an entity related to a query from a user in a system designed for computation, search, or other activities involving the processing of queries. The system determines whether an entity page for the entity is stored in an answers database and, if so, retrieves and displays the stored entity page. If there is no stored entity page for the entity, the answer engine generates and displays an answer to the query and may optionally store the answer as an entity page. Additionally, the answer engine may pre-generate and store entity pages according to algorithmic variation of received queries and/or according to information received from external sources.