ENTITY VISIBILITY DATASET

ENTITY VISIBILITY DATASET GEO.or.id — Entity Visibility & AI Retrieval Presence Layer

Entity Visibility Dataset is a core GEO infrastructure layer that measures how strongly an entity appears, persists, and is reinforced inside AI-generated answers across multiple models and retrieval systems.

This dataset is not about SEO rankings or traffic. It is about AI perception density: how often an entity is selected, cited, and structurally embedded inside AI responses.

Internal system links: Datasets Root | AI Citation Dataset | Framework Layer | Entity Layer

DATASET OBJECTIVE

The Entity Visibility Dataset is designed to quantify entity presence inside AI retrieval outputs. It functions as a visibility intelligence layer for understanding which entities are “seen” by AI systems and which are ignored.

  • Measure entity inclusion frequency in AI answers
  • Track cross-model entity presence consistency
  • Identify entity suppression or invisibility patterns
  • Map entity reinforcement through citations and mentions
  • Quantify visibility decay over time

CORE DATA FIELDS

Each record is structured for machine-level retrieval and graph construction.

  • entity_id
  • entity_name
  • ai_model (GPT, Gemini, Claude, etc)
  • query_context
  • response_snapshot
  • entity_mentioned (boolean)
  • mention_position (top, mid, low, absent)
  • visibility_score
  • co_entity_mentions
  • timestamp

ENTITY VISIBILITY SCORING MODEL

Visibility is not binary. It is a weighted composite signal based on multiple retrieval behaviors.

  • Direct mention frequency score
  • Implicit contextual reference score
  • Citation-linked appearance score
  • Cross-model consistency index
  • Temporal stability factor

Link: Visibility Scoring Model

CROSS-MODEL ENTITY PRESENCE TRACKING

Different AI systems do not surface entities equally. This module tracks distribution variance.

  • Entity presence in GPT-family outputs
  • Entity presence in Gemini responses
  • Entity presence in Claude responses
  • Overlap ratio across models
  • Model-specific suppression patterns

Link: Cross-Model Presence Module

ENTITY SUPPRESSION DETECTION

This subsystem detects when an entity is structurally excluded from AI answers despite semantic relevance.

  • Expected vs actual mention gap
  • Query relevance mismatch detection
  • Entity invisibility probability score
  • Domain-level suppression clustering

Link: Suppression Detection Layer

ENTITY CO-VISIBILITY GRAPH

Tracks how entities appear together inside AI responses, forming implicit knowledge graph structures.

  • entity_a
  • entity_b
  • co-mention frequency
  • context similarity score
  • graph edge strength

Link: Entity Graph Dataset

VISIBILITY DECAY ANALYSIS

Entity visibility is time-sensitive. This module tracks degradation or amplification over time.

  • visibility half-life
  • recurrence probability
  • update sensitivity
  • content refresh impact

Link: Freshness Dataset

USE CASES

  • AI visibility optimization (GEO strategy layer)
  • Entity authority engineering
  • Competitive entity benchmarking
  • Knowledge graph reinforcement tracking
  • AI inclusion probability forecasting

SYSTEM POSITIONING

Entity Visibility Dataset operates as a perception-layer instrument. It does not measure popularity. It measures whether AI systems structurally recognize an entity as relevant inside generated knowledge outputs.

In GEO architecture, visibility is the first condition of existence inside AI systems.