Context Provenance Graph for Explainable Schema Observations

[KolaSailaja]

Problem Statement

Context observations often originate from multiple applications, services, and user interactions. While confidence scores help estimate reliability, they do not explain where a particular observation came from or how it evolved over time.

For example:

• A travel preference may originate from Maps, Calendar, and Booking apps.
• A dietary preference may be inferred from Food Delivery, Health Metrics, and manual user input.
• A workspace preference may be learned from IDE settings and Workspace Notes.

Currently, there is no standardized mechanism to track the provenance (origin and lineage) of context observations.

Without provenance information:

• Developers cannot audit how an observation was formed.
• Explainability becomes limited.
• Debugging incorrect inferences becomes difficult.
• Trust in automated context generation decreases.

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Proposed Solution

Introduce a Context Provenance Graph that records the complete lineage of every observation.

Each observation should maintain provenance metadata such as:

• Originating application
• Source category
• Observation timestamp
• Transformation history
• Derived-from relationships
• Confidence evolution
• Manual user overrides
• Last validation event

Rather than storing only a confidence score, every context claim should have a traceable history.

Example:

Preference:
Running

↓

Source:
Health Metrics

↓

Derived From:
Daily workout logs

↓

Validated By:
Explicit user confirmation

↓

Confidence:
0.96

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Expected Features

The provenance graph should support:

• Source tracking
• Parent-child observation relationships
• Multiple contributing sources
• Confidence history
• Manual correction history
• Schema version association
• Optional visualization-friendly graph structure

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Benefits

• Improved explainability
• Easier debugging
• Better auditing
• Increased transparency
• Higher developer trust
• Future visualization support

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Possible Implementation

• Introduce provenance metadata structures.
• Represent relationships as a Directed Acyclic Graph (DAG).
• Store source references without duplicating observations.
• Maintain immutable provenance history.
• Expose APIs/utilities for retrieving observation lineage.

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Acceptance Criteria

• Every observation can reference one or more sources.
• Parent-child provenance relationships are maintained.
• Provenance history is immutable.
• Confidence evolution is recorded.
• Unit tests cover single-source and multi-source observations.
• Documentation includes provenance examples.

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