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MetaGraph AIU

A knowledge graph project that converts the U.S. OMB Federal AI Use Case Inventory into a semantically structured, graph-queryable dataset — with a focus on understanding the business goals driving federal AI adoption.

Why this project

The OMB inventory is published as a flat CSV. Its categorical fields use uncontrolled strings and its descriptive fields are free text. This makes cross-agency comparison, trend analysis, and governance assessment difficult at scale.

This project builds a semantic layer on top of that data:

  • Business goal extraction — each use case is tagged with 0–5 goals from a curated 10-cluster, 39-sub-goal taxonomy using an LLM classification pipeline. These tags become typed edges in the knowledge graph and are the primary lens for understanding what agencies are actually trying to accomplish with AI.
  • Structured graph data — all categorical fields (development stage, topic area, impact type, HISP service, demographic features, and more) are modeled as SKOS concept nodes with object-property edges, not string literals, enabling graph traversal and algorithm execution without string parsing.
  • Cross-year continuity — provenance and entity-resolution predicates support comparing 2023 and 2024 inventories at the use case level.
  • Extensibility — the BFO/CCO-aligned ontology and SKOS taxonomy are designed to absorb new inventory years, new goal categories, and new analytical questions without structural changes to existing data.

Status

Component Status
OWL ontology (aiu_ontology_ver1.ttl) Draft v1
SHACL shapes (aiu_shapes.ttl) Draft v1
Business goal taxonomy (39 sub-goals) Draft v1
ETL pilot (15-record proof of concept) Complete
Full 2023 and 2024 ETL run Pending
Property graph database load Pending
Entity resolution (cross-year) Pending

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License

Public domain — CC0 1.0 Universal.

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Graph DB of skills, job positions, roles, competencies, policies, etc. for graph analysis, graph RAG, and other adoptions.

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