Hyper-Extract vs ontobricks
Knowledge-extraction CLI: LLMs turn documents into structured graphs, hypergraphs and spatio-temporal knowledge — with an MCP server for agents and Obsidian vault export. — versus — Turns Databricks Unity Catalog tables into a materialized knowledge graph: OWL ontology design, R2RML mapping, OWL 2 RL/SWRL/SHACL reasoning, auto-generated GraphQL — exposed to agents over MCP.
Two roads to a knowledge graph: hyper-extract LLM-extracts from documents, OntoBricks ontology-maps from governed tables. Pick by where your truth lives — files or the warehouse.
| Hyper-Extract | ontobricks | |
|---|---|---|
| Stars | 3.2k | 254 |
| Forks | 382 | 49 |
| Language | Python | Python |
| License | NOASSERTION | NOASSERTION |
| Last activity | 2 days ago | yesterday |
| Topics | rag | rag |
| Curated connections | 3 | 2 |
Hyper-Extract — the curator's take
The pitch beyond ordinary KG extraction is the hypergraph: relations that connect MORE than two entities survive instead of being flattened into pairwise triples. One command per document, query the abstracts over MCP from Claude Desktop or your IDE, export to Obsidian wikilinks. NOT a graph database (it extracts, storage stays simple) and no standard license resolution at review time — verify before building on it; extraction quality tracks the LLM you plug in.
ontobricks — the curator's take
The only tool here that gives agents a REASONED graph — OWL 2 RL/SWRL inference over your warehouse, not just edges — and the four-click LLM-assisted pipeline from table metadata to queryable ontology is genuinely novel. When NOT: anywhere outside Databricks — it hard-requires Unity Catalog, Lakebase Postgres and Databricks Apps. Labs project: no SLA, ★254 young. For lakehouse graph context without the platform lock-in, look at omnigraph.