Knowledge Graph Data Fusion via Ontology Alignment and Trusted Mediator
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Solution Overview
Problem
Current data fusion methods for knowledge graphs across multiple platforms and technology fields are inefficient, requiring costly graph reconstruction, long development cycles, and compromising data security due to the need for data sharing across different business parties.
Innovation Solution
A knowledge graph data fusion method and system that automates the construction of a fused knowledge graph by selecting target entity and relationship fields from ontology definition data of multiple knowledge graphs, using graph operators to process data instances, and generating a fused knowledge graph within a trusted environment, ensuring data privacy and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional data fusion methods are used to combine knowledge graphs from multiple platforms, then data fusion capability is achieved, but data security is compromised due to the need for data sharing across different business parties
Solution Approach 1:
The patent introduces a trusted environment as an intermediary component that receives data from multiple knowledge graphs, performs fusion operations, and returns results without exposing raw data between business parties. This mediator enables data fusion capability while maintaining data security by preventing direct data sharing.
2Adaptability or versatility
If graph reconstruction is performed to fuse knowledge graphs from multiple platforms, then data fusion is achieved, but development cycle increases due to the complexity of the reconstruction process
Solution Approach 1:
The patent performs ontology alignment and graph operator determination in advance before actual data fusion execution. By preparing the fusion framework, operators, and alignment mappings beforehand, the system reduces the development cycle during runtime while maintaining comprehensive data fusion capability.
3Measurement precision
If comprehensive graph reconstruction is performed to ensure accurate data fusion, then fusion accuracy is improved, but computational costs increase significantly
Solution Approach 1:
The patent segments the data fusion process into distinct stages: ontology alignment, graph operator determination, and selective data fusion. By dividing the comprehensive reconstruction into modular segments, the system achieves necessary fusion accuracy while reducing overall computational costs through targeted processing at each stage.
4Adaptability or versatility
If manual data fusion processes are used across multiple platforms, then data fusion capability is achieved, but fusion efficiency is low due to the labor-intensive nature of the process
Solution Approach 1:
The patent implements automated ontology alignment and graph operator selection that perform data fusion operations autonomously without manual intervention. The system self-services by automatically determining fusion strategies, aligning ontologies, and executing fusion tasks, thereby dramatically improving fusion efficiency while maintaining adaptability.
Data Source
AI summary
Implementations of the specification provide a knowledge graph data fusion method and system, and the method includes: obtaining a target entity field and a target relationship description, the target entity field and the target relationship description being selected from ontology definition data of two or more knowledge graphs; and then, obtaining data instances of related platforms or technology fields, and processing the obtained data instances based on a graph operator that is in ontology definition data of a fused knowledge graph and that is used to perform fusion processing on entity fields and relationship descriptions of different platforms or technology fields, to generate the fused knowledge graph.