Repository Information Archive for Natural Language Query Precision
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Solution Overview
Problem
Retrieving data from large document repositories using natural language queries is prone to errors and imprecision due to the numerous possible meanings of query words, leading to low precision and high recall, making it challenging to retrieve accurate results.
Innovation Solution
A method that analyzes the repository structure by generating a repository information archive, which includes a persons matrix, relationship statistics, and facet distribution, to aid in interpreting and executing queries, thereby converting natural language queries into structured queries for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If natural language queries are used to retrieve data from large repositories, then ease of operation is improved, but measurement precision deteriorates due to numerous possible meanings of query words
Solution Approach 1:
The patent introduces an intermediary processing layer that analyzes repository structure, object relationships, and field distributions before executing queries. This intermediary analysis creates a mapping between natural language queries and repository structure, improving precision without sacrificing ease of operation. The system mediates between the user's natural language input and the repository's data structure through statistical analysis of fields, values, and relationships.
Solution Approach 2:
The patent performs preliminary analysis of repository structure, including counting documents containing properties, determining facet distributions, and analyzing object relationships before queries are executed. This preliminary action creates a pre-computed understanding of the repository that enables more precise interpretation of natural language queries, reducing errors from ambiguous wording.
2Measurement precision
If comprehensive repository analysis is performed to improve query accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the repository analysis into distinct components: analyzing objects and their fields, counting document properties, determining facet distributions, and mapping relationships. This segmentation allows the complex analysis to be broken into manageable parts that can be executed systematically, improving precision while controlling complexity through modular processing.
Solution Approach 2:
The patent changes parameters by analyzing statistical properties of the repository such as field value distributions, document counts per property, and relationship frequencies. By transforming the repository understanding into statistical parameters and distributions, the system achieves higher query precision through data-driven insights without requiring overly complex analytical structures.
3Measurement precision
If repository structure information is tightly coupled with query interpretation, then measurement precision is improved, but loss of time increases due to additional analysis required
Solution Approach 1:
The patent performs preliminary analysis of repository structure, field distributions, and object relationships before queries are submitted. By pre-computing statistical information about the repository including facet distributions and document counts, the system reduces the time required for query interpretation while maintaining high precision through the use of pre-analyzed structural information.
Data Source
AI summary
Techniques for analyzing a repository are described herein. A method for analyzing a repository may include obtaining a list of known persons in a repository based on objects, users, and groups retrieved from the repository. The method may further select one of the objects having a field and a value, and then determine whether the field of the selected object is a facet based on a probability that the field of the selected object has a limited number of possible values. In analyzing the repository, a repository information archive may be generated. The repository information archive may include the relationship between the selected object and at least one other object, statistics and counts related to properties in the selected objects, and whether or not the field of the selected object is a facet.