Digital Survey Knowledge Graphs for Complex Relationship Analysis
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Solution Overview
Problem
Conventional electronic survey systems using flat databases struggle with inefficient data analysis and manipulation due to their inability to represent complex relationships between entities, requiring significant time and computing resources to identify connections.
Innovation Solution
A knowledge graph-based system that generates a predefined ontology of topics, connects survey data to nodes via edges, and infers relationships, enabling flexible and efficient storage and retrieval of survey data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If flat databases are used to store survey data, then data storage is simple and easy to implement, but the system cannot represent complex relationships between entities and requires significant time and computing resources to identify connections
Solution Approach 1:
The patent transitions from flat, two-dimensional database tables to a multi-dimensional knowledge graph structure where entities, attributes, and relationships are represented across multiple hierarchical levels. This dimensional expansion enables the system to capture complex relationships while maintaining efficient query performance through graph-based traversal algorithms.
Solution Approach 2:
The patent segments survey data into distinct entity types (respondents, responses, attributes, relationships) and organizes them as separate nodes in a knowledge graph. This segmentation allows the system to process and analyze specific relationship types independently, improving overall data analysis efficiency without sacrificing storage simplicity.
2Loss of information
If filters are applied to sort through database rows and columns to identify relationships, then specific information can be located, but the process requires significant time and computing resources
Solution Approach 1:
The patent pre-processes survey data during ingestion to automatically build the knowledge graph structure, pre-computing relationships and connections between entities. This preliminary action eliminates the need for time-consuming filter operations during query execution, as relationships are already established and can be retrieved through efficient graph traversal.
Solution Approach 2:
The patent introduces a knowledge graph as an intermediary layer between raw survey data and analysis queries. This intermediary structure pre-organizes data relationships, allowing the system to answer complex relationship questions without directly filtering through raw database rows and columns, thereby reducing both time and computational resources required.
3Ease of operation
If conventional systems require administrator involvement to determine relationships between entities, then data can be accessed, but the system lacks automation and efficiency
Solution Approach 1:
The patent implements self-service capabilities where the knowledge graph automatically infers and establishes relationships between survey entities based on predefined schemas and machine learning algorithms. This automation eliminates the need for administrator intervention in routine relationship identification tasks while maintaining data accessibility through intuitive query interfaces.
Solution Approach 2:
The patent replaces manual administrator operations with automated machine learning models that analyze survey data patterns and establish relationships autonomously. This substitution of mechanical human operations with intelligent algorithms improves both automation extent and operational efficiency while preserving ease of data access through programmatic interfaces.
Data Source
AI summary
This disclosure relates to methods, non-transitory computer readable media, and systems generate a knowledge graph to implement, process, and analyze digital surveys and digital survey data cross computer networks. In particular, the disclosed systems can generate and utilize a knowledge graph to determine and coordinate topic ontologies, store and access digital survey data, and generate digital benchmarks. For example, the disclosed systems generate a knowledge graph based on a predefined ontology of topics by connecting topic nodes via a plurality of edges. Additionally, the disclosed systems receive survey data associated with administering an electronic survey to respondent client devices. The disclosed systems extract topics from the survey data and determine connections between the survey data and the topic nodes in the knowledge graph. In one or more embodiments, the disclosed systems generate digital benchmarks between sets of data based on the relationships.