Knowledge Graph UI for Service Data Integrity
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current spreadsheet-based systems for managing linkages between service providers, service receivers, and component service providers face issues with data integrity, scalability, security, and analysis, particularly in large enterprises with complex relationships, leading to difficulties in compliance, risk management, and decision-making.
Innovation Solution
Implementing a knowledge graph data structure that semantically links and analyzes data, using ontologies and APIs to visualize and manage relationships between entities, enabling efficient data storage and analysis across disparate systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a spreadsheet-based system is used to manage linkages between service providers, service receivers, and component service providers, then the system is simple to implement and easy to operate, but data integrity deteriorates and scalability is limited
Solution Approach 1:
The patent introduces a knowledge graph as an intermediary layer between traditional spreadsheet-based systems and modern data analysis requirements. The knowledge graph serves as a mediator that maintains the simplicity of data entry while providing enhanced data integrity through semantic relationships and ontological constraints, resolving the contradiction between ease of operation and data integrity.
Solution Approach 2:
The patent transforms the data structure from flat spreadsheet rows to hierarchical knowledge graph representations with multiple relationship types. This parameter change in data organization enables better data integrity through structured relationships while maintaining operational simplicity through automated reasoning and inference capabilities.
2Ease of operation
If a spreadsheet-based system is used to manage linkages between service providers, service receivers, and component service providers, then the system is easy to operate, but scalability deteriorates
Solution Approach 1:
The patent transitions from two-dimensional spreadsheet tables to multi-dimensional knowledge graph structures that capture hierarchical relationships, temporal dimensions, and semantic connections. This dimensional transformation enables the system to scale effectively while maintaining ease of operation through automated query processing and visualization capabilities.
Solution Approach 2:
The knowledge graph system provides universal functionality that can handle diverse data types, relationship patterns, and analysis requirements within a single unified framework. This multi-functionality enables scalability across different enterprise scenarios while preserving the ease of operation through standardized interaction patterns.
3Reliability
If a knowledge graph data structure is implemented to semantically link and analyze data, then data integrity and scalability are enhanced, but device complexity increases
Solution Approach 1:
The knowledge graph system performs self-service through automated reasoning, inference, and validation processes that maintain data integrity without requiring complex manual intervention. The system automatically infers relationships, validates consistency, and generates insights, thereby reducing the operational complexity burden despite the enhanced data integrity capabilities.
Solution Approach 2:
The patent implements feedback mechanisms that automatically detect and correct data inconsistencies, validate relationships, and provide real-time feedback on data quality. This feedback loop maintains high data integrity while managing complexity through automated correction processes rather than requiring complex manual oversight.
4Adaptability or versatility
If a knowledge graph data structure is implemented to semantically link and analyze data, then scalability is enhanced, but ease of operation deteriorates
Solution Approach 1:
The patent creates simplified visual representations and abstractions of the complex knowledge graph structure that ease user interaction. By copying essential relationships into intuitive visual formats and standardized query interfaces, the system maintains scalability while preserving ease of operation through familiar interaction patterns.
Solution Approach 2:
The knowledge graph serves as an intermediary that abstracts the complexity of scaled data management from the user interface. This intermediary layer provides simplified access patterns, standardized queries, and automated processing that maintain ease of operation even as the underlying system scales to handle complex enterprise data.
Data Source
AI summary
A method may include presenting a user interface including: a service receiver input element presenting a selected service receiver; a service identifier input element; and a graph presentation area; executing a first knowledge graph database query based on the selected service receiver; populating the service identifier input element with a set of service identifiers; receiving a selection of a service identifier; executing a second knowledge graph database query using a combination of the selected service receiver and the service identifier; and generating, in the graph presentation area an interactive graph based on tuple results of the second knowledge graph database query, the interactive graph including: representations of entities in the tuple results including the selected service receiver and the service identifier; and links connecting the representations of entities.