Keyword Decay Mapper

A composite metric that quantifies the degree to which a document's information, references, or statistics have become outdated relative to the current factual consensus. The index typically aggregates multiple signals:

• Factual drift: Discrepancies between stated claims and current ground truth
• Reference rot: Percentage of cited sources that are now broken or superseded
• Statistical aging: Time elapsed since key numerical data points were last verified

The Staleness Index serves as the primary input signal for the Keyword Decay Mapper, correlating objective content degradation with observed ranking declines.

A mathematical model that defines the rate at which a content asset loses its ranking authority over time. Common formulations include:

• Exponential decay: Rapid initial drop followed by asymptotic leveling, typical for news content
• Linear decay: Steady, predictable erosion common in semi-evergreen reference material
• Step-function decay: Abrupt drops triggered by specific events like product discontinuation

The Keyword Decay Mapper uses these functions to forecast future ranking trajectories and prioritize update interventions before critical traffic loss occurs.

A natural language processing model that analyzes a search query to determine if the user requires the latest information, a specific historical snapshot, or timeless knowledge. The classifier categorizes queries into:

• QDF-sensitive: Queries where freshness is a dominant ranking factor
• Historical: Queries seeking information from a specific time period
• Evergreen: Queries where publication date has minimal impact on relevance

The Keyword Decay Mapper integrates temporal intent classification to distinguish between harmful decay and benign aging, preventing unnecessary updates to content that serves historical or timeless intent.

A system that compares the current live version of a document against a cached baseline to identify and extract only the modified sections for processing. Key capabilities include:

• Semantic diffing: Identifying meaning-level changes beyond surface text edits
• Structural change detection: Flagging modifications to heading hierarchy or content organization
• Numerical update extraction: Isolating changed statistics and data points

When the Keyword Decay Mapper identifies a decaying asset, the Delta Detection Engine determines the minimum viable update scope required to restore freshness signals.

A programmatic rule that initiates a content regeneration or update pipeline when a monitored data source changes or a staleness threshold is breached. Trigger types include:

• Threshold-based: Activates when the Content Staleness Index exceeds a defined value
• Event-driven: Responds to external data source updates or product catalog changes
• Scheduled: Calendar-based triggers for content with predictable decay patterns

The Keyword Decay Mapper feeds directly into these triggers, providing the diagnostic justification for automated content refresh workflows.

An observability tool that tracks how the contextual meaning of a document shifts over successive edits, ensuring the core topic focus is not lost during updates. It monitors:

• Topic vector displacement: Measuring embedding distance between original and updated content
• Keyword cannibalization risk: Detecting when refreshed content begins competing with sibling pages
• Intent misalignment: Flagging when updates shift the page away from its target query intent

This monitor acts as a safety mechanism within the Keyword Decay Mapper workflow, preventing over-correction that could damage existing rankings for stable keyword clusters.