Knowledge Base Segmentation for Multi-Source Data Management
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Solution Overview
Problem
The challenge lies in effectively managing and enriching knowledge bases with information from various data sources, including unstructured and incomplete data, to support informed decision-making processes.
Innovation Solution
A system and method that involve receiving multi-modal data, analyzing it to determine features, storing these features in a database, and using a large language model to generate search results displayed on a graphical user interface, which includes a map and legend showing relevant search features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data is collected from multiple sources to enrich knowledge base, then information completeness is improved, but data management complexity increases
Solution Approach 1:
The system segments the knowledge base into multiple data sources or collections, allowing independent management and processing of data from different sources. This segmentation enables the system to handle complex multi-source data while maintaining organizational structure and reducing overall management complexity.
Solution Approach 2:
The patent introduces intermediary components such as data processors, analyzers, and integration layers that mediate between raw multi-source data and the knowledge base. These intermediaries standardize and harmonize data from various sources, reducing management complexity while preserving information completeness.
2Loss of information
If unstructured and incomplete data is incorporated, then knowledge base enrichment is improved, but data processing difficulty increases
Solution Approach 1:
The system performs preliminary actions on unstructured and incomplete data before full integration, including initial cleaning, validation, and structuring. This preliminary processing reduces the difficulty of subsequent data operations while maintaining the enriching benefit of incorporating diverse data sources.
Solution Approach 2:
The patent applies parameter changes to transform unstructured data into structured formats, adjusting data characteristics to make them more manageable. This includes converting data types, normalizing formats, and applying transformation rules that reduce processing difficulty while preserving essential information.
3Measurement precision
If feature extraction and analysis is performed on multi-modal data, then information accuracy is improved, but computational requirements increase
Solution Approach 1:
The system applies partial feature extraction, focusing on extracting only the most relevant features from multi-modal data rather than processing all possible features. This selective approach maintains information accuracy for critical features while reducing overall computational requirements.
Solution Approach 2:
The patent applies local quality by differentiating processing intensity across different data types or features. High-accuracy processing is applied to critical features where precision is essential, while less intensive processing is used for secondary features, optimizing the balance between accuracy and computational cost.
Data Source
AI summary
A system is configured to: (a) receive multi-modal data from one or more sources, the multi-modal data including text data; (b) analyze the data to determine features; (c) store the features in a database; (d) receive a search query for searching the stored features; (e) analyze the search query using a large language model to extract search features; (f) generate search results from the search features; and (g) display search results on a standardized graphical user interface including (i) a map and (ii) a legend having at least one or more of the search features displayed.