Knowledge Processor for Structured Data Coalescing
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Solution Overview
Problem
Enterprises face challenges in capturing and representing evolving knowledge across multiple channels and sources, with information existing in silos and varying by location and time, making it difficult to create a unified knowledge base.
Innovation Solution
A system and method that uses a knowledge processor to determine features and contribution factors from input data, generate concept feature matrices, and dynamically update knowledge data based on incremental synchronization, ensuring that knowledge is represented and maintained as structured data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If knowledge is captured from multiple channels and sources across different locations and times, then the quantity and diversity of knowledge increases, but the complexity of managing and representing this knowledge increases
Solution Approach 1:
The system segments knowledge into discrete concepts with associated features, representing each concept as an independent entity with structured attributes. This segmentation allows knowledge from multiple sources to be broken down into manageable units that can be individually processed, stored, and retrieved without overwhelming complexity.
Solution Approach 2:
The system transforms unstructured or semi-structured knowledge into structured data by extracting and standardizing features as parameters. Each concept is represented with consistent feature parameters that enable uniform processing regardless of the original source format, location, or time, thereby managing diversity through parameterization.
2Reliability
If knowledge is continuously updated from dynamic sources, then the currency and relevance of knowledge improves, but the difficulty of detecting and measuring changes increases
Solution Approach 1:
The system employs incremental synchronization that compares current knowledge data with previously stored data, using feedback mechanisms to identify and process only the changes that have occurred. This feedback loop enables the system to maintain currency by detecting updates efficiently without reprocessing all knowledge data from scratch.
Solution Approach 2:
The system performs preliminary actions by maintaining a stored representation of previous knowledge states and preparing comparison frameworks in advance. This allows change detection to occur efficiently when new data arrives, as the system is already positioned to compare and identify differences without delay.
3Adaptability or versatility
If knowledge exists in silos across different channels and locations, then the adaptability of knowledge to different contexts improves, but the loss of information through lack of integration increases
Solution Approach 1:
The system creates a universal knowledge representation framework that can handle knowledge from diverse sources and contexts through a common structured format. This universal representation enables knowledge to be adapted to different contexts while maintaining integrity, as the standardized feature-based structure can accommodate various types and sources of knowledge without loss.
Solution Approach 2:
The system merges knowledge from multiple silos by extracting features and representing them in a unified structured format. By combining knowledge from different channels, locations, and times into a common representation model, the system eliminates information loss that would occur through isolation while preserving the adaptability of individual knowledge sources.
Data Source
AI summary
In certain embodiments, the method may comprise, determining one or more features associated with each of one or more concepts from at least one sentence; determining at least one concept feature matrix based on the one or more features; determining one or more contribution factors among the one or more concepts, based on the at least one concept feature matrix; determining incremental synchronization data by storing current timestamp associated with the one or more concepts and comparing it with knowledge data; generating at least one new concept feature matrix based on the incremental synchronization data; determining one or more new contribution factors among the one or more concepts, based on the at least one new concept feature matrix; determining differential value between the one or more contribution factors and the one or more new contribution factors; and dynamically updating the knowledge data, in the repository system.