Knowledge Graph Interfaces for Enterprise Service Relationship Analysis
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Solution Overview
Problem
Existing spreadsheet-based systems for managing complex enterprise organizational structures face challenges with data integrity, scalability, security, and data analysis, particularly in tracking linkages between service providers, receivers, and component providers, leading to inefficiencies and increased risk due to human error and lack of data visualization tools.
Innovation Solution
Implementing a knowledge graph database to semantically link and analyze data, using a semantic ontology to generate triples and visualize relationships between entities, enabling robust data management and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a spreadsheet is used to manage enterprise organizational data, then the system is simple to implement, but data integrity and reliability deteriorate due to human error
Solution Approach 1:
The patent replaces manual spreadsheet operations with an automated knowledge graph system that uses semantic ontologies and algorithms to automatically generate, validate, and visualize enterprise relationship data. This substitution eliminates human error in data entry and manipulation while maintaining ease of use through automated processes.
Solution Approach 2:
The knowledge graph system performs self-validation and self-correction of enterprise relationship data through automated algorithms that check data consistency, validate relationships, and maintain integrity without requiring manual intervention. The system serves itself by automatically detecting and resolving data quality issues.
2Ease of manufacture
If a spreadsheet is used to track enterprise relationships, then the initial setup is simple, but scalability worsens when handling complex organizational structures
Solution Approach 1:
The patent implements a dynamic knowledge graph system that automatically adapts to changing enterprise structures. The semantic ontology framework allows the system to dynamically incorporate new entities, relationships, and data types as the organization grows, while the automated visualization tools scale to handle increasing data complexity without requiring proportional increases in manual effort.
Solution Approach 2:
The knowledge graph system serves multiple functions simultaneously: it stores enterprise relationship data, validates data integrity, generates visualizations, and provides analytical insights. This multi-functional approach allows the system to scale across diverse organizational structures and relationship types while maintaining a unified management interface.
3Device complexity
If manual spreadsheet management is used, then the system requires minimal technology infrastructure, but data analysis capabilities and visualization deteriorate
Solution Approach 1:
The patent introduces a knowledge graph database as an intermediary layer between raw enterprise data and analytical tools. This intermediary structure organizes data in a semantically meaningful way that enables efficient querying, analysis, and visualization. The knowledge graph acts as a mediator that transforms unstructured spreadsheet data into structured, analyzable information while maintaining data fidelity.
Solution Approach 2:
The system adds a semantic dimension to traditional flat spreadsheet data by organizing information in a multi-dimensional knowledge graph structure. This allows data to be analyzed from multiple perspectives (entities, relationships, attributes) simultaneously and enables sophisticated visualizations that reveal patterns and insights not visible in conventional two-dimensional spreadsheets.
4Ease of manufacture
If spreadsheets are used to manage service provider relationships, then the system is easy to deploy, but security and access control deteriorate
Solution Approach 1:
The patent implements security controls and access policies in advance during knowledge graph construction. Role-based access control, data classification, and security validation are built into the system architecture before deployment, preventing security issues rather than addressing them afterward. This preliminary security configuration maintains deployment simplicity while establishing robust protection mechanisms.
Data Source
AI summary
A method may include presenting a user interface on a computing device, the user interface including: a service provider input element identifying a service provider; a service identifier input element identifying a service; and a graph presentation area; executing a knowledge graph database query using a combination of the service provider and the service as input to a knowledge graph database; receiving tuple results in response to the executing, the tuple results including an allocation value property of the service provider attributable to the service provider with respect to the service; and generating in the graph presentation area, an interactive graph based on the tuple results including: representations of entities including the service provider and the service in the tuple results as nodes in the interactive graph, wherein a representation of the service provider includes the allocation value; and links connecting the representations of entities.