Semantic Fact Retrieval With Query Disambiguation and User Roles
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Solution Overview
Problem
Existing systems struggle to efficiently and accurately retrieve contextualized facts from diverse data sources in response to user queries, particularly in complex scenarios involving ambiguity and user authorization.
Innovation Solution
A system utilizing natural language processing to generate semantic embeddings, perform disambiguation, and execute actions associated with user intents, enabling retrieval of relevant facts from multiple data sources while handling ambiguity and user roles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If natural language processing with semantic embeddings is implemented to understand user intent, then the accuracy of fact retrieval is improved, but the system complexity increases
Solution Approach 1:
The system segments the complex NLP processing into distinct modules: embedding generation, disambiguation, action identification, and fact retrieval. Each module handles a specific aspect of the processing pipeline, making the overall complex system manageable and maintainable while preserving high retrieval accuracy.
Solution Approach 2:
Semantic embeddings serve as an intermediary representation between raw user input and the fact retrieval system. This intermediate semantic space enables accurate intent understanding without requiring the entire system to be reconfigured for each query type.
2Reliability
If disambiguation and user role analysis are performed to handle ambiguous queries, then the reliability of information retrieval is improved, but the processing time increases
Solution Approach 1:
User roles and authorization contexts are pre-analyzed and stored with user profiles. When a query arrives, the system retrieves pre-computed role information instead of analyzing it from scratch, significantly reducing processing time while maintaining reliable authorization checks.
Solution Approach 2:
The system uses feedback loops where disambiguation results from previous queries inform future processing. Common ambiguous terms and their resolutions are cached, allowing the system to quickly resolve similar ambiguities without full re-analysis.
Data Source
AI summary
A method for fact retrieval includes receiving an input from a user, said input including a string of text that represents a user intent. The method includes performing natural language processing on the input to generate an embedding that corresponds to a semantic representation of the string of text, and based on the generated embedding, identifying an action that is associated with the user intent. The method includes executing the identified action, such that the executed action returns a result associated with the user intent, and providing the result to the user.