Feature Graph Database Disambiguation Through Ontology Checks

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Solution Overview

Problem

Existing feature graph databases face challenges in accurately identifying and preventing the addition of duplicate features, which can lead to unwieldy data management and resource waste, especially in telecommunication networks with vast and complex data sets.

Innovation Solution

A three-pronged approach involving feature lineage declaration, ontological checks, and fingerprinting is employed to identify and prevent the addition of duplicate features, utilizing a feature ontology and machine learning models to analyze feature similarities and relationships.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Quantity of substance

If features are added indiscriminately to the feature graph database, then the database becomes more comprehensive, but duplicate features accumulate leading to unwieldy data management and resource waste

Engineering Contradiction:

Improvenumber of features in databaseVSAvoiddata management complexity

Core Design Contradiction:

Quantity of substanceVSDevice complexity

Solution Approach 1:

The system performs preliminary duplicate detection by comparing feature ontologies, names, and fingerprints before adding new features to the database. This preventive approach avoids accumulating duplicates and maintains database manageability while still allowing comprehensive feature storage.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system provides feedback mechanisms including duplicate detection alerts, feature similarity scoring, and automated prevention of duplicate additions. This feedback loop ensures that as the database grows, duplicate management remains efficient and resource consumption is optimized.

Inventive Principle:

Principle #23Feedback

2Ease of manufacture

If traditional duplicate detection methods are used, then implementation is simple, but accuracy is insufficient leading to false positives and negatives

Engineering Contradiction:

Improveimplementation simplicityVSAvoidduplicate detection accuracy

Core Design Contradiction:

Ease of manufactureVSMeasurement precision

Solution Approach 1:

The duplicate detection process is segmented into multiple independent checks: ontology comparison, name matching, and fingerprint analysis. Each segment focuses on a specific aspect of feature identity, improving overall accuracy while maintaining implementation clarity through modular design.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system combines multiple detection methodologies (ontology comparison, name matching, fingerprinting) into a composite detection mechanism. This multi-faceted approach achieves high detection accuracy by leveraging the strengths of each method while compensating for their individual weaknesses.

Inventive Principle:

Principle #40Composite materials

3Measurement precision

If comprehensive feature analysis is performed to ensure accurate duplicate detection, then detection accuracy improves, but processing time increases

Engineering Contradiction:

Improveduplicate detection accuracyVSAvoidfeature addition processing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

Fingerprints are pre-computed and stored with features, allowing rapid comparison during duplicate detection. This preliminary preparation eliminates the need for time-consuming full feature analysis during the addition process, maintaining both accuracy and efficiency.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system performs partial analysis by focusing on key discriminative features (ontology, name, fingerprint) rather than exhaustive comparison of all feature attributes. This selective approach achieves sufficient detection accuracy while significantly reducing processing time.

Inventive Principle:

Principle #16Partial or excessive action

4Ease of operation

If feature names and descriptions are relied upon for duplicate identification, then the process is straightforward, but reliability is insufficient due to naming variations

Engineering Contradiction:

Improvedetection process simplicityVSAvoidduplicate identification reliability

Core Design Contradiction:

Ease of operationVSReliability

Solution Approach 1:

Feature ontologies serve as an intermediary layer that provides a standardized, structured representation of feature meaning and relationships. This intermediary enables reliable duplicate detection by comparing semantic structures rather than relying solely on variable names and descriptions.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system transforms feature identification from name-based string matching to ontology-based structural comparison. By changing the identification parameter from superficial names to deep semantic structures, reliability improves while the process remains manageable through automated ontology processing.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS12430313B2Disambiguation of feature graph databases

Publication Date: 2025.09.30 AT&T INTELLECTUAL PROPERTY I L P

AI summary

A processing system may obtain a request to add at least a first feature to a feature graph database, where the request comprises a first feature ontology of the first feature, and where the first feature ontology comprises: a label of the first feature and a relationship of the first feature to a concept or to another feature. The processing system may then identify whether the first feature is a duplicate of a second feature in the feature graph database based at least upon the first feature ontology and a second feature ontology of the second feature and generate an indication of whether the first feature is a duplicate in response to the identifying.