Dynamic Data Relationship Acquisition via Semantic Analysis
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Solution Overview
Problem
Current data processing systems face limitations in dynamically acquiring and managing interrelations between data items, particularly in search applications, due to static hierarchical structures that fail to adapt to user preferences and context, and lack efficient methods for synchronizing data across devices.
Innovation Solution
A method for acquiring interrelations between data items using a data processing system that performs syntactic and semantic comparisons to generate subject-predicate-object relations, allowing for automatic semantic analysis and dynamic link creation between data items, enabling context-aware and user-individualized data management without manual ontology implementation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static hierarchical structures are used for data organization, then data storage and navigation are simplified, but the system cannot adapt to user preferences and context dynamically
Solution Approach 1:
The patent implements dynamic data structures that can change and adapt based on user preferences and context. The system automatically adjusts data organization and relationships rather than using fixed hierarchical structures, allowing the data model to evolve with user needs while maintaining operational simplicity through automation.
2Measurement precision
If manual ontology implementation is required, then semantic relationships can be defined precisely, but the process becomes time-consuming and complex
Solution Approach 1:
The system performs automatic semantic analysis of data without requiring manual ontology implementation. It autonomously identifies relationships, categories, and semantic connections between data elements, achieving precise semantic understanding through automated algorithms that process data structures and extract meaningful relationships independently of manual intervention.
3Productivity
If syntactic comparison alone is used for data matching, then processing speed is maintained, but semantic understanding and context-awareness are insufficient
Solution Approach 1:
The patent combines multiple comparison methods including syntactic comparison (for speed) and semantic comparison (for precision) into a unified data matching process. The system integrates both approaches to achieve both fast processing and accurate semantic understanding, allowing automatic acquisition of interrelations through combined analytical techniques rather than relying on single-method approaches.
Data Source
AI summary
The present invention relates to a method for acquiring at least one interrelation between at least one item of data representing at least one item of information of at least one data inventory and at least one item of data representing at least one item of information of a request for an interrelation (interrelation request) to the at least one item of data representing at least one item of information of at least one data inventory by means of a data processing system. Furthermore, a data processing system with data representing information in at least one data inventory that is accessible via at least one data source as well as a data processing device for electronic data processing comprising a control and/or computing unit, an input unit and an output unit, which are respectively configured and/or adapted for at least partially carrying out a method according to the invention are also contemplated.