Natural Language Request Relationship Recommendation
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Solution Overview
Problem
Natural language data applications face challenges in efficiently processing user requests due to ambiguity, leading to time-consuming and repetitive interrogation processes, especially when users are unfamiliar with domain-specific languages (DSLs) associated with different data sources.
Innovation Solution
A computer-implemented method that processes natural language requests by determining unavailable relationships and generating data relationship recommendations, reducing the time and effort required for user interaction and increasing accuracy by updating knowledge databases with new relationships and data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If natural language data applications use traditional interrogation processes to resolve ambiguity, then user intent can be determined, but the process becomes time-consuming and repetitive
Solution Approach 1:
The system performs preliminary actions by proactively generating data relationship recommendations and updating the knowledge database before users make requests. This anticipatory approach stores relationship information in advance, so when a user asks about data relationships, the system can immediately retrieve pre-computed recommendations rather than performing time-consuming interrogation processes each time
Solution Approach 2:
The system implements feedback mechanisms by learning from user interactions and automatically updating the knowledge database with new relationships discovered during processing. This continuous feedback loop improves the system's ability to understand user intent over time without requiring repeated clarification questions, thereby reducing both time and interaction complexity
2Measurement precision
If the system requests disambiguating information from users, then request accuracy improves, but user effort and interaction complexity increase
Solution Approach 1:
The system applies self-service by automatically determining data relationships through generated recommendations rather than requiring users to manually provide disambiguating information. The knowledge database autonomously learns and updates relationship patterns, enabling the system to interpret user requests accurately without burdening users with technical clarification questions about data sources, relationships, or DSL syntax
Solution Approach 2:
The knowledge database serves as an intermediary between the user's natural language request and the underlying data sources. It translates and mediates the interaction by providing pre-computed relationship recommendations that bridge the gap between ambiguous user intent and precise data queries, eliminating the need for users to directly engage in complex disambiguation processes
3Reliability
If the knowledge database is updated with new relationships, then NL request processing accuracy increases, but system complexity increases
Solution Approach 1:
The knowledge database implements self-service by automatically learning and updating relationships from data processing experiences without requiring manual curation. The system autonomously extracts relationship patterns from processed data and updates its internal knowledge base, improving reliability while avoiding the complexity of manual knowledge base management and maintenance
Solution Approach 2:
The system uses feedback from processing NL requests to continuously refine and update the knowledge database. Each interaction provides learning opportunities that automatically enhance the database's relationship understanding, improving processing accuracy over time through experience rather than requiring complex manual updates or external intervention
Data Source
AI summary
Various embodiments of the present application set forth a computer-implemented method that includes processing a first natural language (NL) request, where the first NL request includes a first artifact. The method further includes determining that a first relationship, associated with the first artifact and useable to process the first NL request, is unavailable in a first NL language processing system. The method further includes generating a first data relationship recommendation based on the first NL request. In addition, the method includes causing the first data relationship recommendation to be provided to a user.