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OpenGov summary: Difference between revisions
→Knowledge graph
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A knowledge graph is like a smart map of information: | A knowledge graph is like a smart map of information: | ||
* Nodes = things (entities), e.g., "FEMA", "Disaster Assistance Program", "Stafford Act". | |||
* Edges = relationships between them, e.g., "FEMA sponsors → Disaster Assistance Program", "Disaster Assistance Program is authorized by → Stafford Act", "Stafford Act funds flow to → multiple agencies". | |||
* The power comes from being able to ask questions across connections, like "Show me all programs authorized by laws passed after 2010 that FEMA sponsors and that have >$100M funding." | |||
Traditional databases or spreadsheets can store facts, but knowledge graphs excel at revealing connections and enabling complex, relationship-based searches. | Traditional databases or spreadsheets can store facts, but knowledge graphs excel at revealing connections and enabling complex, relationship-based searches. | ||
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2. How MediaWiki + Cargo Creates This | 2. How MediaWiki + Cargo Creates This | ||
* MediaWiki handles the "Wikipedia-like" part: | |||
**Each federal entity gets its own page (e.g., a page called "Federal Emergency Management Agency" or "Wildfire Mitigation Grant Program"). | |||
**Pages have readable narrative text, history, discussion tabs, and look official with a USWDS (U.S. Web Design System) skin. | |||
**This makes it citizen-friendly — people read summaries, see context, and get directed to official .gov links. | |||
* Cargo turns those pages into structured, graph-like data: | |||
**Templates act like fill-in-the-blank forms. For example, a "Federal Program" template might have fields like: | |||
***Program Name | |||
***Sponsoring Agency (links to the Agency page) | |||
***Authorizing Legislation (links to the Statute page) | |||
***Annual Funding Amount | |||
***Eligibility Summary | |||
***Primary Official URL (always points back to the real .gov site) | |||
***Status (Active / Proposed / Expired) | |||
**When someone (or the AI pipeline) fills in the template on a page, Cargo automatically stores the answers in database tables — one table per template type. | |||
***Example: All "Federal Program" templates feed into a single "Programs" table in the background, with columns matching the fields. | |||
This is where the knowledge graph emerges: | This is where the knowledge graph emerges: | ||
**Because fields can link to other pages (e.g., "Sponsoring Agency" contains a link to the "FEMA" page), Cargo knows relationships exist. | |||
**The system doesn't need fancy triple-store tech (like RDF/OWL in heavier graphs); it uses simple relational tables but supports joins, list fields, and hierarchy traversal to mimic graph behavior. | |||
3. Querying the Graph — The Real Power | 3. Querying the Graph — The Real Power | ||
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Examples in OpenGov Encyclopedia context: | Examples in OpenGov Encyclopedia context: | ||
* Simple lookup: "List all active programs sponsored by NOAA with funding >$50 million." | |||
**Cargo scans the "Programs" table, filters on Sponsor = "NOAA", Funding > 50M, Status = "Active". | |||
* Relationship traversal (graph-like): | |||
**"Show every program authorized by legislation containing '42 U.S.C.' that is sponsored by an agency in the Department of the Interior." | |||
***Joins the Programs table to Agencies table (via Sponsor field) and checks the Authorizing Legislation field. | |||
* Cross-entity discovery: | |||
**"Find all disaster-related programs connected to FEMA, including their authorizing laws and related agencies." | |||
***Uses joins and list fields (e.g., if a program has multiple linked agencies or statutes). | |||
* Dynamic pages: | |||
**A task-oriented page like "Prepare for Wildland Fire" can embed live query results: a table of relevant programs, pulled fresh from Cargo data, with links back to official sites. | |||
Queries can output as: | Queries can output as: | ||
* Tables/lists on wiki pages | |||
* Maps (if coordinates are stored) | |||
* JSON/CSV exports (for feeding agency LLMs or dashboards) | |||
* Inline dynamic content (updates whenever source data changes) | |||
4. Why This Counts as a Knowledge Graph (Even If Lightweight) | 4. Why This Counts as a Knowledge Graph (Even If Lightweight) | ||
* It has entities (pages/nodes) and typed relationships (via template fields and links). | |||
* You can traverse connections using joins, HOLDS (for lists), WITHIN (for hierarchies), etc. | |||
* It's queryable at scale — supports parametric searches agencies need for AI (e.g., "programs related to arid land agriculture with >$50M funding"). | |||
* Data stays fresh and attested — tied to official .gov sources via the AI pipeline, with confidence scores and always-link-back banners. | |||
* Unlike heavier graphs (e.g., Neo4j or RDF stores used in some federal pilots), it's simple, open-source, low-maintenance, and integrated with readable wiki pages. | |||
In short: OpenGov Encyclopedia combines Wikipedia-style readability with database-like structure. Humans get helpful narrative pages that point to official sources; machines/AI get a clean, connected, verifiable graph of federal entities and relationships — all without duplicating agency content. | In short: OpenGov Encyclopedia combines Wikipedia-style readability with database-like structure. Humans get helpful narrative pages that point to official sources; machines/AI get a clean, connected, verifiable graph of federal entities and relationships — all without duplicating agency content. | ||