Sparse Graph Recovery Model for Concept Correlation
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Solution Overview
Problem
Existing computing systems are inefficient and inaccurate in managing conceptual connections across electronic document collections, failing to discover non-obvious connections and requiring significant computational resources and human effort.
Innovation Solution
A concept graphing system that utilizes a sparse graph recovery machine-learning model to identify less-obvious correlations between concepts, including positive and negative connections, and provides these connections within a visual concept graph.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional natural language processing approaches are used to extract concepts from documents, then common conceptual connections can be identified, but non-obvious connections are missed and computational resources are excessive
Solution Approach 1:
The patent transforms the concept extraction problem by changing parameters from traditional NLP features to matrix factorization parameters. The system represents documents and concepts as matrices and uses singular value decomposition to identify relationships, fundamentally changing the mathematical parameters used for analysis and achieving better accuracy with reduced computational overhead
Solution Approach 2:
The patent replaces conventional mechanical NLP processing approaches with a matrix-based mathematical system. Instead of using traditional text processing algorithms, the system uses matrix multiplication, decomposition, and factorization to extract concept relationships, substituting the mechanical processing paradigm with a more efficient mathematical framework
2Measurement precision
If manual document classification by human reviewers is employed, then document accuracy can be maintained, but significant human effort and time are required
Solution Approach 1:
The patent enables the system to automatically perform document classification and concept relationship identification without human intervention. The matrix factorization system self-adjusts and self-optimizes by iteratively refining its models based on the document collection, eliminating the need for manual reviewer involvement while maintaining high accuracy
Solution Approach 2:
The patent introduces matrix representations as an intermediary between raw documents and classification results. Instead of direct human review or simple keyword matching, the system uses matrix factorization as an intermediary layer that transforms document data into concept relationships, automating the classification process while preserving accuracy
3Adaptability or versatility
If existing computing systems provide common conceptual connections, then basic document management is achieved, but less-common connections and negative correlations are not detected
Solution Approach 1:
The patent inverts the traditional approach by not only identifying positive correlations between concepts but also explicitly detecting negative correlations. The matrix factorization system captures both positive and negative relationships in the concept space, providing a more complete and accurate view of concept interactions that conventional systems miss
Data Source
AI summary
The present disclosure relates to systems, methods, and computer-readable media for utilizing a concept graphing system to determine and provide relationships between concepts within document collections or corpora. For example, the concept graphing system can generate and utilize machine-learning models, such as a sparse graph recovery machine-learning model, to identify less-obvious correlations between concepts, including positive and negative concept connections, as well as provide these connections within a visual concept graph. Additionally, the concept graphing system can provide a visual concept graph that determines and displays concept correlations based on the input of a single concept, multiple concepts, or no concepts.