Knowledge Graph Traversal Rules for Entity Link Verification
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Solution Overview
Problem
Existing spreadsheet-based systems for managing linkages between service providers, receivers, and component providers in large enterprises are prone to human error, data integrity issues, and scalability challenges, making it difficult to accurately and timely identify critical service disruptions.
Innovation Solution
A knowledge graph system is employed to semantically link and analyze data, using a semantic ontology to generate triples and apply graph logic rules for efficient management and visualization of relationships between legal entities, enabling quick identification of impacted entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a spreadsheet-based system is used to manage enterprise service relationships, then ease of operation is maintained, but data integrity and reliability deteriorate due to human error and scalability challenges
Solution Approach 1:
The patent replaces manual spreadsheet-based mechanical data management with an automated knowledge graph system that uses semantic ontologies and graph traversal algorithms. This substitution eliminates human error in data entry and relationship tracking while maintaining ease of operation through automated rule verification and impact analysis.
2Measurement precision
If a knowledge graph system is implemented to accurately identify service disruptions, then reliability and measurement precision improve, but device complexity and implementation difficulty increase
Solution Approach 1:
The patent segments the complex knowledge graph system into modular components: semantic ontology definitions, graph traversal rule engines, impact analysis algorithms, and visualization interfaces. This segmentation allows each component to be developed, verified, and maintained independently while working together to provide accurate service disruption identification.
Solution Approach 2:
The patent introduces graph traversal rules as intermediary elements that mediate between the raw knowledge graph data and the impact analysis results. These rules serve as a configurable layer that translates business logic into automated verification without requiring complex hardcoding, thereby managing system complexity while maintaining high identification accuracy.
3Productivity
If graph traversal rules are applied to verify enterprise relationships, then productivity and speed of identification improve, but device complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-defining graph traversal rules that encode business logic and relationship verification requirements. These rules are established in advance and automatically applied during impact analysis, eliminating the need for complex real-time computations and enabling fast productivity-driven identification of service disruptions.
Data Source
AI summary
A method may include executing a knowledge graph database query to a knowledge graph database storing properties of entities; receiving a set of tuple results in response to the executing identifying a set of entities and a set of relationships that connect the set of entities in the knowledge graph database; presenting a UI including a graph presentation area that includes: graphical representations of the set of entities in the set of tuple results; and links connecting the representations of the entities according to the set of relationships; performing a classification validation test against the set of tuple results identifying classification properties for entities in a chain of related entities in the knowledge graph database; determining that the chain meets the classification properties for the entities in the chain in the classification validation test; and in response, updating a presentation style of the graphical representations of the set of entities.