Ontology Visualization System for Database Relationship Mapping
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Visualizing and navigating ontologies and merging data between relational and graph databases is challenging due to the complexity of relationships and the use of different database management systems and query languages.
Innovation Solution
A visualization system that includes a web server, graph visualization component, API, search component, data assets inventory knowledge base, operation data store, and ontology database, which generates various types of visualizations such as semantic graphs, taxonomy graphs, and data asset graphs, and facilitates switching between relational and graph database views.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If graph databases are used to store ontology representations with nodes, edges, and properties, then the ability to represent complex relationships is improved, but the difficulty of visualizing and navigating the data increases
Solution Approach 1:
The system segments the complex ontology data into hierarchical levels, allowing users to navigate from high-level concepts to detailed properties step by step. The visualization interface divides the graph into manageable sections with controls for expanding and collapsing nodes, making complex relationships easier to comprehend without overwhelming the user.
Solution Approach 2:
The system adds temporal and hierarchical dimensions to the graph visualization. Users can navigate through different levels of abstraction (hierarchical dimension) and control the amount of detail displayed (temporal dimension), transforming the static complex graph into a multi-dimensional interactive experience that reduces cognitive load.
2Adaptability or versatility
If multiple database management systems and query languages are used for relational and graph databases, then the flexibility to manage different data types is improved, but the complexity of merging data increases
Solution Approach 1:
The system introduces an intermediary layer that translates between different query languages and database formats. This mediator component handles the complexity of data merging between relational and graph databases, allowing users to work with unified queries while the system manages the underlying complexity of different database systems.
Solution Approach 2:
The visualization system is designed to be universal, supporting multiple database types and query languages through a common interface. This multi-functional approach allows the same visualization tools to work with both relational and graph data without requiring separate systems, reducing the operational complexity despite supporting diverse data formats.
3Measurement precision
If comprehensive ontology data is displayed to provide complete information, then the accuracy of data understanding is improved, but the usability of the interface deteriorates
Solution Approach 1:
The interface is designed to be dynamic, allowing users to adjust the level of detail displayed based on their needs. Users can interactively expand or collapse nodes, filter by property types, and control the amount of information visible at any time. This dynamic adaptability maintains data accuracy while preserving usability by letting users manage information overload.
Solution Approach 2:
The system applies local quality by allowing different parts of the interface to display different levels of detail simultaneously. Important summary information is displayed prominently while detailed properties are available on-demand through interactive elements. This selective presentation maintains overall accuracy while improving local usability in different interface regions.
Data Source
AI summary
A method may include presenting a user interface on a display device of a computing device, the user interface including: a search query input element; a plurality of graph type options; a graph level selection element; and a graph presentation area; receiving a search query inputted into the search query input element, the search query identifying a concept object in an ontology; retrieving data associated with the concept object from a graph database based on selections made in the graph type options and the graph level selection element, the data including a set of result objects related to the concept object; and rendering a hierarchical graph in the graph presentation area, the hierarchical graph illustrating the set of result objects and the concept object as interactive nodes.