ontocast vs semantica
Agentic ontology-assisted RDF extraction: co-evolves domain ontologies and fact graphs in a map/reduce pipeline with RDF 1.2 provenance, entity disambiguation and SHACL autofix. — versus — Graph-native context infrastructure for accountable AI: ingest enterprise data, extract a knowledge/context graph (RDF + LPG), run graph analytics and causal reasoning with decision provenance.
Both build accountable RDF context with provenance. Semantica is the enterprise runtime with graph analytics and causal reasoning on top; OntoCast is the focused extraction half you can embed in a LangGraph agent.
| ontocast | semantica | |
|---|---|---|
| Stars | 228 | 9.6k |
| Forks | 27 | 1.0k |
| Language | Python | Python |
| License | Apache-2.0 | MIT |
| Last activity | 14 days ago | 4 days ago |
| Topics | knowledge-graphs | knowledge-graphs |
| Curated connections | 4 | 6 |
ontocast — the curator's take
The extractor to pick when ontology drift is the thing that has burned you: schema and instances evolve in one loop, GraphUpdate insert/delete patches replace whole-graph regeneration, and SHACL validation repairs machine-fixable violations without another LLM pass. Runs as a REST service, a batch CLI, or a LangGraph node, with pyoxigraph in memory by default and Fuseki when you need persistence. Realities: it's research-grade (Zenodo DOI, 228 stars) with a configuration surface to match — 202 environment variables, and the docs sensibly ship a 47-variable minimal file plus playbooks. The LLM critic is off at the default `MAX_VISITS_PER_NODE=1`, so quality out of the box is one render pass.
semantica — the curator's take
The accountability angle is the real differentiator — decision provenance and deterministic reasoning aimed at regulated domains, where 'the agent decided' isn't an acceptable audit trail. W3C-standards + both RDF and LPG is rare breadth. When NOT: the README's marketing density ('open-source Palantir') outruns its documentation depth — prototype the core path before betting a compliance program on it; ★2.3k and the platform surface is huge for the team size.