DATA_PLAN.md

Kink Data Plan

Objectives

• Build a canonical kink catalog with aliases, definitions, and safety notes.
• Attach image or illustration assets only when source, license, and provenance are clear.
• Support three independent similarity systems:
  • text similarity
  • visual similarity
  • behavioral similarity

Source classes

Definitions and safety

• National Coalition for Sexual Freedom
• TASHRA
• Other reusable educational sources with explicit license terms

Taxonomy breadth and aliases

• BDSMchecklist
• KinkList and similar openly licensed checklist projects
• Manually curated normalization when source terms overlap or conflict

Images and illustrations

• Wikimedia Commons
• Openverse
• Flickr Creative Commons
• Wellcome Collection
• Commissioned or generated illustrations for long-tail categories with poor reusable imagery

Canonical tables

kinks

• id
• name
• cluster
• short_definition
• long_definition
• risk_level
• is_extreme
• notes

aliases

• id
• kink_id
• alias
• source_id

definitions

• id
• kink_id
• source_id
• text
• tone
• license
• source_url

images

• id
• kink_id
• source_id
• asset_url
• thumbnail_url
• mime_type
• license
• creator
• attribution_text
• is_explicit
• is_illustration
• visual_tags

sources

• id
• name
• source_type
• base_url
• license_model
• notes

similarity_edges

• left_kink_id
• right_kink_id
• similarity_type
• score
• method
• version

Normalization workflow

1. Import raw candidate terms from checklist and educational sources.
2. Collapse aliases into canonical kinks.
3. Mark distinctions that must stay separate. Examples:

• feet interest vs foot worship vs footjob
• edging vs orgasm denial vs forced orgasm
• oral fixation vs messy oral vs spit play vs emetophilic play

4. Attach source-backed definitions.
5. Attach only license-safe image candidates.
6. Compute text and visual embeddings.
7. Later, add behavioral co-like edges from app usage or other lawful source data.

Similarity architecture

Text

• Use sentence-transformers on names, aliases, definitions, examples, and safety notes.

Visual

• Use OpenCLIP embeddings over illustrations and example images.
• Store per-image vectors and derive per-kink centroids plus diversity-aware exemplars.

Behavioral

• Build co-occurrence edges from first-party ratings.
• Keep behavioral similarity separate from text and visual similarity so rare or taboo interests are not drowned out.

Evaluation questions

• Does discovery surface close matches without collapsing distinct kinks together?
• Does the system expose rare or esoteric kinks early enough for the right users?
• Do visuals help disambiguate categories or create misleading shortcuts?
• Are overlap results useful for partners when interests are adjacent rather than identical?