Dynamic Knowledge Base Amendment via SQL Validation
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Solution Overview
Problem
Conversational agents face difficulties in efficiently designing agents that meet user expectations and dynamically updating knowledge bases with answers not present in the existing data, due to unavailable data elements, inconsistent vocabulary, and system design issues.
Innovation Solution
An AI platform with tools like a query manager, evaluator, translation manager, SQL manager, and knowledge base manager that preprocess natural language queries, identify missing tokens, leverage neural networks to predict relationships, and dynamically amend the knowledge base by translating queries into structured query language (SQL) and evaluating their validity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the knowledge base is dynamically expanded to include query tokens absent from existing data, then the accuracy and relevance of responses is improved, but the reliability of the knowledge base deteriorates due to potential invalid amendments
Solution Approach 1:
The system executes SQL queries to test whether amended knowledge base tokens produce valid results, using the execution feedback to determine if amendments should be committed. This feedback loop ensures that only valid amendments that improve query accuracy are permanently added to the knowledge base, resolving the contradiction between expanding knowledge coverage and maintaining reliability.
Solution Approach 2:
The system performs preliminary validation by executing SQL queries before permanently committing knowledge base amendments. This preliminary action tests the validity of predicted token relationships in a controlled manner, ensuring that only verified amendments are integrated into the knowledge base, thus maintaining reliability while enabling dynamic expansion.
2Adaptability or versatility
If neural networks are used to predict relationships between query tokens and knowledge base tokens, then the ability to handle unavailable data elements is improved, but the device complexity increases
Solution Approach 1:
The system introduces an intermediary validation layer (SQL execution and validity evaluation) between the neural network predictions and the knowledge base amendments. This intermediary component verifies the validity of predicted relationships before committing them, allowing the system to leverage neural network adaptability while maintaining controlled complexity through a structured validation process.
3Productivity
If the system dynamically amends the knowledge base with predicted token relationships, then the productivity of query response is improved, but the difficulty of detecting and measuring validity increases
Solution Approach 1:
The system employs self-service validation where the knowledge base amendment process automatically tests its own predictions by executing SQL queries and evaluating validity. This self-validation mechanism enables rapid productivity improvements while systematically detecting and measuring the validity of amendments without requiring external verification, thus reducing the difficulty of validity assessment.
Data Source
AI summary
A system, program product, and method for use with an artificial intelligence (AI) platform to dynamically amend a knowledge base responsive to query evaluating and processing. A received or detected query is subject to natural language processing to identify, annotate, and map one or more query tokens against a knowledge base. The query tokens are evaluated against the knowledge base to identify one or more query tokens absent from the knowledge base and leverage a neural network to predict a probability relationship between the query tokens absent from the knowledge base and one or more tokens populated in the knowledge base. The natural language (NL) query is translated to a structured query language (SQL) and the SQL query is executed and evaluated, and the knowledge base is selectively and dynamically amended subject to the SQL evaluation.