Semantic AI for Scientific Knowledge Systems
T2 Labs builds ontology-aware AI workflows that transform scientific documents, datasets, and domain knowledge into structured knowledge graphs and AI-ready context.
What We’re Building
Reusable semantic AI components for curation, harmonization, ontology modeling, graph construction, and agentic scientific workflows.
SciAgent Studio
An agentic research assistant for finding relevant datasets, summarizing scientific resources, and assembling AI-ready research context.
OmniGraph Agent
An agentic graph intelligence layer for querying, aligning, and analyzing ontologies and knowledge graphs with schema-aware planning and provenance.
GraphRadar
Profile and compare ontologies and knowledge graphs using aggregated metrics, structural insights, and quality signals—with OntoChoice selection criteria to help users pick the right ontology for their use case.
OntoAnnotate
Ontology-aware annotation for linking text, metadata, and tabular data to controlled vocabularies and ontology terms.
Curation Workflows
Reusable curation pipelines that transform aligned mappings into harmonized, ontology-modeled datasets so future data in the same format flows through automatically.
Knowledge Graph Workflows
Pipelines for transforming publications, entities, datasets, and relationships into reusable scientific knowledge systems.
Graph Query Assistant
Natural-language query interfaces for exploring RDF knowledge graphs through SPARQL generation, designed to extend toward ontology tools and graph databases.
Interested in semantic AI for scientific knowledge?
T2 Labs is exploring collaborations around ontology-aware AI systems, knowledge graph workflows, and scientific data infrastructure.
Contact T2 Labs