[{"data":1,"prerenderedAt":4},["ShallowReactive",2],{"raw-en-articles\u002Fopen-data-ontology-for-publication-licensing-provenance-and-reuse":3},"---\ntitle: Open data ontology for publication, licensing, provenance, and reuse\ndescription: Structured ontology semantics for publication, licensing, provenance, and reuse\nlang: en\nnavigation:\n  enabled: false\n  section: articles\n  order: 30\ntags:\n  - data\n  - ontology\n  - open-data\n---\nThe cost of integration is manual semantic reconciliation every time data crosses team boundaries.\n\n## Why this matters\n\nWhen data concepts are standardized, engineers reason about data systematically.\n\n## What this looks like in practice\n\n- Data lineage is trackable from systems, humans, and AI pipelines consistently.\n- Data quality thresholds mean the same thing whether applied by automation or humans.\n- Metadata is reusable because schemas describe the same concepts consistently.\n\n## How teams use it\n\n- implementing data governance across clouds, on-premises, and third-party stores\n- automating discovery and classification without rebuilding business logic\n- proving provenance for audit, debugging, and compliance systematically\n\nData quality is a team property—it requires agreement on meaning.\n",1776235588157]