Knowledge Graph Temporal Queries with Confidence-Tracked Relationships
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Solution Overview
Problem
Building knowledge graphs is a time-consuming and often manual process, and existing relationships may not indicate their veracity, leading to compromised usefulness due to outdated or irrelevant information.
Innovation Solution
A knowledge graph data structure using triple data units with subunits for confidence, confidentiality, and temporal properties, along with automated generation and feedback-based training to maintain and update relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If knowledge graphs are built manually to ensure accuracy, then reliability is improved, but productivity deteriorates
Solution Approach 1:
The system performs preliminary actions by automatically generating initial knowledge graph relationships from available data sources before manual verification. This allows the bulk of relationship creation to be automated, with manual review focused only on uncertain cases, thereby improving productivity while maintaining reliability.
Solution Approach 2:
The system implements feedback mechanisms where confidence values of automatically generated relationships are evaluated, and relationships below certain confidence thresholds are flagged for manual review. This feedback loop ensures that only relationships needing human verification are manually checked, improving both productivity and reliability.
2Reliability
If all relationships are manually verified to ensure accuracy, then reliability is improved, but loss of time worsens
Solution Approach 1:
Instead of verifying all relationships manually, the system applies partial verification only to relationships with confidence values below a specified threshold. This partial action approach maintains reliability for critical relationships while avoiding the time loss of comprehensive manual verification.
Solution Approach 2:
The system changes the parameter of verification intensity based on confidence values. High-confidence relationships receive minimal or no manual verification, while low-confidence relationships receive intensive verification. This parameter-based differentiation optimizes both reliability and time efficiency.
3Reliability
If confidence values and temporal context are added to relationships, then reliability is improved, but device complexity worsens
Solution Approach 1:
The system segments the relationship data structure into distinct components: the core relationship tuple (subject, predicate, object) and optional metadata attributes (confidence value, temporal context). This segmentation allows the core functionality to remain simple while adding reliability-enhancing features as optional, modular elements.
Solution Approach 2:
The confidence value and temporal context attributes serve multiple functions: they improve reliability by indicating data quality, enable automated filtering and querying, and support temporal analysis. This multi-functionality justifies the added structural complexity by providing diverse benefits from the same data elements.
4Productivity
If automated generation is used to improve productivity, then productivity is improved, but reliability deteriorates
Solution Approach 1:
The system introduces confidence values as an intermediary metric that bridges automated generation and manual verification. The confidence value quantifies the reliability of automatically generated relationships, enabling intelligent routing to manual review only when necessary. This intermediary mechanism maintains productivity while protecting against reliability degradation.
Solution Approach 2:
The system dynamically adjusts the balance between automated generation and manual verification based on confidence values. Relationships with high confidence are accepted automatically, while those with low confidence trigger manual review. This dynamic approach optimizes both productivity and reliability based on the quality of each generated relationship.
Data Source
AI summary
Various examples are directed to systems and methods for utilizing relationship data. A computing system may receive a time-dependent query against a knowledge graph data structure. The computing system may access confirmation data from the knowledge graph data structure, the confirmation data describing a first plurality of confirmation points-in-time at which the first test relationship is true. The computing system may determine that at least one of a beginning or an end of a first time period associated with a test relationship of the time-dependent query is not defined by the knowledge graph data structure. The computing system may determine a response to the first time-dependent query indicating a veracity of the test relationship at a test point-in-time using the first plurality of confirmation points-and-time.