Local Knowledge Graph Linking for Consistent Distributed Queries
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Solution Overview
Problem
Large and complex knowledge graphs face challenges in managing user access and maintaining consistency, particularly when integrating with ontologies, leading to increased computing resource usage and potential divergence between local and core graphs.
Innovation Solution
Implementing local knowledge graphs that reference core graphs, allowing read-only access and maintaining links to core elements, while enabling the creation of derivative elements and properties, and managing access rights to ensure consistency and reduce resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If knowledge graphs become larger to accommodate more data and users, then the coverage and functionality improve, but the computing resource usage and system complexity increase
Solution Approach 1:
The knowledge graph is divided into a core knowledge graph containing shared elements and multiple local knowledge graphs containing domain-specific elements. This segmentation allows the system to maintain comprehensive coverage while reducing the computational burden on any single graph by distributing data across multiple manageable units that share common resources.
Solution Approach 2:
The core knowledge graph serves multiple local knowledge graphs, providing shared elements, classes, and properties that can be reused across different domains. This universality reduces redundancy and computing resources by allowing multiple specialized graphs to access common data rather than maintaining separate copies.
2Use of energy by moving object
If local knowledge graphs are created to reduce resource usage, then computing efficiency improves, but consistency and synchronization with core graphs may be compromised
Solution Approach 1:
Local knowledge graphs are merged with the core knowledge graph through shared elements and bidirectional linking. This merging ensures that changes in the core graph are reflected in local graphs and vice versa, maintaining consistency while allowing each graph to operate independently for optimization purposes.
Solution Approach 2:
The system implements feedback mechanisms where changes in local knowledge graphs can propagate to the core knowledge graph and vice versa. This ensures that consistency is maintained through continuous synchronization, allowing local graphs to reduce resource usage without compromising reliability.
3Use of energy by moving object
If elements are shared across knowledge graphs to reduce redundancy, then resource usage decreases, but managing access rights and permissions becomes more complex
Solution Approach 1:
The core knowledge graph acts as an intermediary between multiple local knowledge graphs, managing shared elements and access rights centrally. This intermediary structure simplifies access management by providing a single point of control for permissions while reducing redundancy across the distributed graph system.
Data Source
AI summary
The present disclosure provides techniques and solutions for linking elements of different knowledges graph and for using such links during knowledge graph processing. When an element of a knowledge graph is created, such as a class, a property, or a class instance, it can be determined whether a corresponding element exists in another knowledge graph. If so, the elements can be operatively linked. When a query is executed against a knowledge graph, if an element is linked to an element of another knowledge graph, the other knowledge graph can be accessed for query processing. When statements are made about a knowledge graph element that is defined in a first knowledge graph element and where the element is defined with respect to an element of a second knowledge graph, the scope of the statement can be limited to the second knowledge graph.