Ontology LLM Knowledge Representation
Budget: ₹1,500 – ₹12,500 INR
I’m developing an ontology-driven large language model whose core task is robust knowledge representation aimed at seamless information retrieval and smooth data integration. The heart of the job is to craft a formal ontology that the model can ingest and reason over, then fine-tune or build an LLM so it can surface precise answers and merge heterogeneous data sources effortlessly.
The work spans three intertwined areas. First comes the ontology itself: classes, properties, and axioms captured in OWL/RDF (Protégé or an equivalent editor is fine) so the structure is machine-readable and logically sound. Next, that ontology needs to be embedded in—or tightly coupled with—an LLM, ideally via a knowledge-enhanced training pipeline using Python, Hugging Face Transformers, and whichever deep-learning stack (PyTorch, TensorFlow) you prefer. Finally, I’ll need an interface layer—RESTful or GraphQL—that lets external systems query the model for both ad-hoc information retrieval and automated data-integration workflows, with SPARQL or similar endpoints exposed where useful.
Deliverables
• Clean, validated ontology file (OWL/RDF)
• Fine-tuned or custom-built LLM weights plus training scripts
• API or microservice that accepts natural-language or structured queries and returns ontology-aligned answers/merged data
• Short technical document explaining setup, dependencies, and example queries
Acceptance criteria
• Ontology passes consistency checks in Protégé with zero unsatisfied classes.
• Retrieval accuracy on a provided test set ≥ 90 %.
• Data-integration demo shows two disparate sources mapped correctly into the shared schema.
• All code reproducible with a single requirements.txt or environment.yml.
If this aligns with your expertise in semantic AI, let’s move forward—clarity of reasoning and clean code matter more than raw parameter count.
The work spans three intertwined areas. First comes the ontology itself: classes, properties, and axioms captured in OWL/RDF (Protégé or an equivalent editor is fine) so the structure is machine-readable and logically sound. Next, that ontology needs to be embedded in—or tightly coupled with—an LLM, ideally via a knowledge-enhanced training pipeline using Python, Hugging Face Transformers, and whichever deep-learning stack (PyTorch, TensorFlow) you prefer. Finally, I’ll need an interface layer—RESTful or GraphQL—that lets external systems query the model for both ad-hoc information retrieval and automated data-integration workflows, with SPARQL or similar endpoints exposed where useful.
Deliverables
• Clean, validated ontology file (OWL/RDF)
• Fine-tuned or custom-built LLM weights plus training scripts
• API or microservice that accepts natural-language or structured queries and returns ontology-aligned answers/merged data
• Short technical document explaining setup, dependencies, and example queries
Acceptance criteria
• Ontology passes consistency checks in Protégé with zero unsatisfied classes.
• Retrieval accuracy on a provided test set ≥ 90 %.
• Data-integration demo shows two disparate sources mapped correctly into the shared schema.
• All code reproducible with a single requirements.txt or environment.yml.
If this aligns with your expertise in semantic AI, let’s move forward—clarity of reasoning and clean code matter more than raw parameter count.