Context-Aware Entity Correspondence and Merging
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Solution Overview
Problem
Existing information aggregation methods are static and do not effectively address dynamic information needs, failing to incorporate context features that are crucial for accurate entity resolution and decision-making in changing environments.
Innovation Solution
A context-aware entity correspondence and merge system that correlates and aggregates information entities based on both data and context features, using modular and extensible meta-models to represent, transform, and link context data, enabling dynamic information management and decision support.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static information aggregation methods are used, then system simplicity is maintained, but information relevance and accuracy deteriorate in dynamic environments
Solution Approach 1:
The patent segments entity resolution into two independent correlation processes: data feature correlation and context feature correlation. This segmentation allows the system to handle complex dynamic information by breaking it down into manageable components that can be processed separately and then integrated, improving information accuracy without overwhelming system complexity.
Solution Approach 2:
The patent implements dynamic information aggregation by continuously updating context features (location, time, activity) and re-correlating entities based on changing context. This dynamic approach allows the system to adapt to changing environments and maintain information accuracy, transforming a static system into a dynamic one that responds to real-time changes.
2Loss of information
If context features are incorporated into entity resolution, then information relevance improves, but processing complexity increases
Solution Approach 1:
The patent separates context feature processing from data feature processing into distinct correlation modules. Context features (location, time, activity) are extracted and correlated independently from traditional data features, allowing the system to maintain information completeness while managing processing complexity through modular design.
Solution Approach 2:
The patent adds a new dimension to entity resolution by incorporating context features as a separate correlation layer. Instead of only analyzing traditional data features, the system now operates in an expanded feature space that includes contextual dimensions, enabling more comprehensive information analysis without fundamentally restructuring the entire processing system.
3Loss of time
If dynamic context-aware aggregation is implemented, then information timeliness improves, but computational requirements increase
Solution Approach 1:
The patent extracts only the most relevant context features (location, time, activity) needed for dynamic aggregation rather than processing all possible data. This selective extraction approach maintains information timeliness by focusing computational resources on critical context elements that most impact entity correspondence, reducing overall computational energy requirements.
Solution Approach 2:
The patent implements partial correlation by focusing on specific context features and entities that are most relevant to the current query or task, rather than performing exhaustive correlation on all available data. This partial action approach provides timely information for decision-making without the full computational burden of complete analysis.
Data Source
AI summary
A computer-based method for correlating relevant information from multiple entities based on contextual correspondence is described. The method includes receiving, at a computer, information relating to a plurality of the multiple entities, the information including data features and context features, correlating the data features utilizing one or more algorithms running on the computer, correlating the context features utilizing one or more algorithms running on the computer, and aggregating the plurality of the multiple entities based on both a correspondence of the data features and a correspondence of the context features for at least one of storage in a memory associated with the computer and output as data from the computer.