Build an automated multi-agent system that generates domain-specific RDF #29

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opened 2025-11-14 13:10:57 +00:00 by aditya · 0 comments

aditya commented

2025-11-14 13:10:57 +00:00

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Description

Build an automated multi-agent system that generates domain-specific RDF
ontologies from document collections.
The system intelligently samples documents, extracts domain concepts and
relationships, discovers existing ontologies from the internet, analyzes
user-provided ontologies, and merges all sources into a unified RDF ontology.
The workflow is orchestrated via LangGraph with conditional routing,
checkpointing for recovery, and iterative validation/refinement loops.

Key Features:

Intelligent document sampling (4-5 documents from large collections)
Automatic concept extraction from documents
Internet-based ontology discovery and collection
Multi-ontology merging with conflict resolution
RDF/OWL format support (Turtle preferred, RDF-XML supported)
Quality validation and iterative refinement

Acceptance Criteria

Document Processing Pipeline: System successfully samples 4-5 documents
from collections (configurable 2-10), extracts domain concepts, entity
classes, hierarchies, and properties using distributed sampling strategy.
Ontology Discovery & Collection: System searches internet for existing
ontologies in identified domain, downloads 3-5 relevant RDF/OWL files
from repositories (LOV, BioPortal, etc.), and extracts metadata (namespace,
version, license).
Ontology Merging: System merges internet-discovered, user-provided,
and document-extracted ontologies into unified RDF format, resolving
namespace conflicts and creating equivalence mappings while preserving
provenance.
RDF Generation & Validation: System generates valid RDF ontologies
(Turtle syntax) with proper namespace declarations, class hierarchies,
properties with domains/ranges, and passes structural/semantic validation
checks.
Workflow Orchestration: LangGraph workflow routes through all agents
(Coordinator, Sampler, Analyzer, Collector, Builder, Merger, Validator,
Refiner, File Manager) with conditional routing, checkpointing enabled,
and error recovery.

Definition of Done

Coordinator, Configuration Manager,
Document Sampler, Document Analyzer, Ontology Collector, Ontology
Analyzer, Ontology Builder, Ontology Merger, Ontology Validator,
Ontology Refiner, and File Manager agents are created with appropriate
system prompts and model configurations.
File operations tool (read/write RDF), internet access
tool (web search, HTTP get), RDF processing tool (parse, validate, merge),
and ontology analysis tool (extract classes/properties, detect conflicts)
are integrated.
Workflow nodes and conditional edges are
configured, entry point established, checkpointing enabled, and all routing
paths (CONFIG → SAMPLING → ANALYZING → COLLECTION → ANALYSIS → BUILDING →
MERGING → VALIDATING → REFINING → SAVING) are functional.
System tested with document collections (small
<10, medium 10-50, large 50+), validates RDF output syntax and semantics,
successfully merges 3-5 ontologies from different sources, and handles
error recovery via checkpointing.

## Description Build an automated multi-agent system that generates domain-specific RDF ontologies from document collections. The system intelligently samples documents, extracts domain concepts and relationships, discovers existing ontologies from the internet, analyzes user-provided ontologies, and merges all sources into a unified RDF ontology. The workflow is orchestrated via LangGraph with conditional routing, checkpointing for recovery, and iterative validation/refinement loops. **Key Features:** - Intelligent document sampling (4-5 documents from large collections) - Automatic concept extraction from documents - Internet-based ontology discovery and collection - Multi-ontology merging with conflict resolution - RDF/OWL format support (Turtle preferred, RDF-XML supported) - Quality validation and iterative refinement ## Acceptance Criteria 1. **Document Processing Pipeline**: System successfully samples 4-5 documents from collections (configurable 2-10), extracts domain concepts, entity classes, hierarchies, and properties using distributed sampling strategy. 2. **Ontology Discovery & Collection**: System searches internet for existing ontologies in identified domain, downloads 3-5 relevant RDF/OWL files from repositories (LOV, BioPortal, etc.), and extracts metadata (namespace, version, license). 3. **Ontology Merging**: System merges internet-discovered, user-provided, and document-extracted ontologies into unified RDF format, resolving namespace conflicts and creating equivalence mappings while preserving provenance. 4. **RDF Generation & Validation**: System generates valid RDF ontologies (Turtle syntax) with proper namespace declarations, class hierarchies, properties with domains/ranges, and passes structural/semantic validation checks. 5. **Workflow Orchestration**: LangGraph workflow routes through all agents (Coordinator, Sampler, Analyzer, Collector, Builder, Merger, Validator, Refiner, File Manager) with conditional routing, checkpointing enabled, and error recovery. ## Definition of Done 1. Coordinator, Configuration Manager, Document Sampler, Document Analyzer, Ontology Collector, Ontology Analyzer, Ontology Builder, Ontology Merger, Ontology Validator, Ontology Refiner, and File Manager agents are created with appropriate system prompts and model configurations. 2. File operations tool (read/write RDF), internet access tool (web search, HTTP get), RDF processing tool (parse, validate, merge), and ontology analysis tool (extract classes/properties, detect conflicts) are integrated. 3. Workflow nodes and conditional edges are configured, entry point established, checkpointing enabled, and all routing paths (CONFIG → SAMPLING → ANALYZING → COLLECTION → ANALYSIS → BUILDING → MERGING → VALIDATING → REFINING → SAVING) are functional. 4. System tested with document collections (small <10, medium 10-50, large 50+), validates RDF output syntax and semantics, successfully merges 3-5 ontologies from different sources, and handles error recovery via checkpointing.

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