Semantic Data Consolidation via Ontology Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing technologies fail to effectively collate and combine data from multiple data objects, leading to a loss of comprehensive intelligence in product and service management, as they do not provide a means to merge semantically similar data elements across various data repositories, both internal and external to an enterprise.
Innovation Solution
A system comprising a data processor, a semantic relations module, and a consolidation module identifies and merges semantically similar data elements to generate a consolidated multi-dimensional graph, representing combined data models and providing valuable insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data from multiple data objects is stored separately in various data repositories, then data storage and management is simplified, but comprehensive intelligence is lost due to inability to collate and combine data
Solution Approach 1:
The patent merges semantically similar data elements from multiple separate data objects into a consolidated data object. The consolidation module identifies data elements across different data repositories that represent the same real-world entity (e.g., 'printer' in one repository and 'printing device' in another) and combines them into a unified representation, thereby recovering comprehensive intelligence while managing complexity through automated semantic matching
Solution Approach 2:
The patent introduces a semantic relations module as an intermediary that uses ontologies and knowledge graphs to mediate between disparate data repositories. This intermediary layer enables semantic understanding and mapping of data elements from different sources, allowing the system to collate and combine data without requiring direct integration of all underlying repositories
2Productivity
If manual data collection and synthesis is performed, then data accuracy is maintained, but scalability is limited
Solution Approach 1:
The patent replaces manual data collection and synthesis with an automated computational system. The consolidation module uses semantic relations, ontologies, and algorithms to automatically identify and merge semantically similar data elements, eliminating the need for manual intervention while maintaining matching accuracy through structured semantic rules and knowledge representations
3Ease of operation
If existing technologies automate data collection from the internet, then productivity increases, but the ability to collate and combine data from multiple data objects remains insufficient
Solution Approach 1:
The patent creates a universal consolidation module that can handle multiple types of data objects and data repositories through a single interface. The semantic relations module uses general-purpose ontologies and knowledge graphs that can represent various domains (products, services, devices), enabling the system to collate and combine data from diverse sources without requiring domain-specific customization for each data type
Data Source
AI summary
Semantic consolidation of data is disclosed. One example is a system including a data processor, a semantic relations module, and a consolidation module. The data processor identifies at least two data objects, each data object including a plurality of data elements. The semantic relations module identifies, via a processor, semantically similar data elements of the plurality of data elements. The consolidation module merges the identified semantically similar data elements via the processor, and generates a consolidated data object based on the merged data elements.