Ontological Inferencing for Cross-Domain Data Quality Detection
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Solution Overview
Problem
Existing automated data quality detection systems are limited in their portability across disparate domains and are often complex to design and administer, failing to effectively detect data quality issues in growing data sets.
Innovation Solution
The use of standards-based data characterization through ontologies that represent acceptable data states, allowing for the mapping of incoming data to a domain ontology to detect quality issues based on inference against TBox statements, which describe desired properties of data elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If proprietary, rules-based detection engines are used, then data quality detection can be performed, but portability across disparate domains is limited
Solution Approach 1:
The patent applies universality by creating a domain-agnostic data quality detection system using ontologies and inferencing engines that can operate across multiple domains without requiring domain-specific customization. The system uses universal ontological frameworks (TBox statements) that can be mapped to any data domain, enabling the same detection engine to validate data quality in healthcare, finance, retail, and other domains through standardized inferencing processes rather than proprietary domain-specific rules
2Productivity
If automated data quality detection systems are implemented, then detection efficiency improves, but design and administration complexity increases
Solution Approach 1:
The patent introduces an intermediary layer between raw data and detection logic through ontological models. The TBox statements serve as intermediaries that encode domain knowledge in a standardized format, allowing the inferencing engine to automatically validate data without requiring complex manual rule configuration. This intermediary ontological framework simplifies administration while maintaining automated detection efficiency
Solution Approach 2:
The system changes the parameter representation from proprietary format-specific rules to standardized ontological parameters (TBox statements). By transforming data quality criteria into universal ontological parameters that describe desired data states, the system enables automated detection without requiring complex administrative overhead for rule management, as the ontological parameters can be systematically derived and maintained
3Ease of manufacture
If manual data quality inspection is performed, then preparation requirements are low, but detection capability fails to discover underlying quality issues
Solution Approach 1:
The system performs preliminary action by pre-defining ontological frameworks (TBox statements) that encode acceptable data states and quality criteria before data validation occurs. These pre-established ontological models enable automated inferencing to detect underlying quality issues without requiring extensive manual preparation for each data set, as the validation logic is already structured and ready to apply
Data Source
AI summary
Systems and methods (e.g., utilities) for use in providing automated data quality detection that can be used multiple times across various domains and across multiple data quality spheres. A data structure or schema of an incoming data set is initially mapped to a desired data or knowledge state in a domain ontology made up of a number of TBox statements. Data quality issues in the incoming data set can then be detected as instances of the incoming data set are attempted to be inferenced against or otherwise matched to anticipated TBox statements of the domain ontology.