OntologyLM: Corpus-Driven Schema Discovery System

Analyze document collections to automatically generate custom OWL ontologies that capture the semantic patterns in YOUR specific data

🎯 Core Vision

OntologyLM solves the fundamental problem: "I have valuable data but don't know how to structure it semantically."

Instead of manually analyzing thousands of documents to understand their semantic patterns, OntologyLM analyzes a small sample (10-20 documents) and generates a custom OWL ontology that captures the concepts, relationships, and vocabulary specific to your corpus.

Key Innovation: Automatic semantic schema discovery from real-world document patterns, not theoretical domain knowledge.

🔍 Perfect Use Cases

1. Informal Medical Discussions → Clinical Structure 🏥

Problem: Long COVID forums contain valuable medical insights but use informal language

Sample Documents:

Generated Ontology:

@prefix covid: <http://long-covid-community.org/ontology#> .

covid:Symptom a owl:Class .
covid:BrainFog rdfs:subClassOf covid:Symptom .
covid:PEM rdfs:subClassOf covid:Symptom .  # Post-Exertional Malaise
covid:POTS rdfs:subClassOf covid:Symptom .

covid:ManagementStrategy a owl:Class .
covid:Pacing rdfs:subClassOf covid:ManagementStrategy .

covid:experiencesSymptom a owl:ObjectProperty ;
    rdfs:domain covid:Patient ;
    rdfs:range covid:Symptom .

covid:usesTreatment a owl:ObjectProperty ;
    rdfs:domain covid:Patient ;
    rdfs:range covid:ManagementStrategy .

Value: Enables semantic queries like "Find all management strategies that help with brain fog" across thousands of forum posts.

2. Corporate Documents → Business Intelligence Schema 📊

Problem: Company has years of meeting notes, reports, emails with hidden patterns

Generated Ontology:

@prefix corp: <http://company.org/business-ontology#> .

corp:Project a owl:Class .
corp:Issue a owl:Class .
corp:VendorCapacityIssue rdfs:subClassOf corp:Issue .
corp:IntegrationDelay rdfs:subClassOf corp:Issue .

corp:hasIssue a owl:ObjectProperty ;
    rdfs:domain corp:Project ;
    rdfs:range corp:Issue .

corp:needsSupport a owl:ObjectProperty ;
    rdfs:domain corp:Team ;
    rdfs:range corp:Team .

Value: Query "What issues most commonly delay projects?" or "Which teams need support most often?"

3. Research Paper Collections → Academic Knowledge Graph 📚

Problem: Thousands of papers in emerging field, need to understand research landscape

Value: Query "What methods are most commonly combined?" or "Which datasets are used for multimodal evaluation?"

4. Legal Document Analysis → Case Law Structure ⚖️

Problem: Law firm has thousands of case documents, need to understand precedent patterns

Value: Query "What precedents are cited in contract disputes?" or "What's the average settlement for breach of contract?"

🏗️ Technical Architecture

System Overview

Data Collection Layer:
  - Document sampling and preprocessing
  - Text cleaning and normalization  
  - Format detection and conversion
  - Quality filtering and validation

Analysis Layer:
  - Concept extraction using fine-tuned LLM
  - Relationship discovery through co-occurrence analysis
  - Vocabulary normalization and clustering
  - Semantic pattern recognition

Generation Layer:
  - OWL ontology construction
  - Namespace management and URI generation
  - Property domain/range inference
  - Hierarchy construction from patterns

Validation Layer:
  - RDF/OWL syntax validation
  - Semantic consistency checking
  - Coverage analysis against source documents
  - Human feedback integration

Pixeltable Schema Design

import pixeltable as pxt

# Document collections table
document_collections = pxt.create_table('document_collections', {
    'collection_id': pxt.String,
    'collection_name': pxt.String,
    'description': pxt.String,
    'domain_hint': pxt.String,
    'source_type': pxt.String,  # forum_posts, emails, papers, etc.
    'total_documents': pxt.Int,
    'created_at': pxt.Timestamp,
    'metadata': pxt.Json,
})

# Sample documents used for analysis
sample_documents = pxt.create_table('sample_documents', {
    'sample_id': pxt.String,
    'collection_id': pxt.String,
    'document_text': pxt.String,
    'document_order': pxt.Int,
    'extracted_concepts': pxt.Json,
    'concept_relationships': pxt.Json,
    'quality_score': pxt.Float,
})

# Generated ontologies
generated_ontologies = pxt.create_table('generated_ontologies', {
    'ontology_id': pxt.String,
    'collection_id': pxt.String,
    'ontology_content': pxt.String,  # Full OWL/Turtle content
    'generation_method': pxt.String,
    'concepts_identified': pxt.Int,
    'relationships_identified': pxt.Int,
    'syntax_valid': pxt.Bool,
    'coverage_score': pxt.Float,
    'status': pxt.String,  # draft/validated/production
})

🚀 Model Architecture and Training

Recommended Base Model: LLaMA2-7B

Why LLaMA2-7B is perfect for corpus analysis:

Fine-Tuning Architecture

from peft import LoraConfig, get_peft_model

# Setup LoRA for efficient fine-tuning
lora_config = LoraConfig(
    r=32,  # Higher rank for complex corpus analysis task
    lora_alpha=64,
    target_modules=[
        "q_proj", "v_proj", "k_proj", "o_proj",
        "gate_proj", "up_proj", "down_proj"
    ],
    lora_dropout=0.1,
    bias="none",
    task_type="CAUSAL_LM"
)

Corpus Analysis Prompt Format

def format_corpus_analysis_prompt(documents: List[str], domain_hint: str = None) -> str:
    prompt = "[INST] Analyze the following document collection and generate an OWL ontology...\n\n"
    prompt += "[CORPUS_START]\n"
    
    for i, doc in enumerate(documents):
        prompt += f"[DOCUMENT] {i+1}:\n{doc[:800]}...\n[DOC_END]\n\n"
    
    prompt += "[CORPUS_END]\n\n"
    prompt += "Generate a complete OWL ontology: [/INST]\n\n"
    
    return prompt

🔬 Evaluation Framework

Corpus-Specific Evaluation Metrics

class CorpusOntologyEvaluator:
    def evaluate_on_test_corpus(self, test_collection_id: str) -> Dict:
        return {
            'syntax_validity': self.check_owl_syntax(generated_ontology),
            'concept_coverage': self.measure_concept_coverage(generated_ontology, test_documents),
            'relationship_accuracy': self.measure_relationship_accuracy(generated_ontology, test_documents),
            'vocabulary_capture': self.measure_vocabulary_capture(generated_ontology, test_documents),
            'parsing_applicability': self.test_parsing_applicability(generated_ontology, test_documents[15:])
        }

🚀 Implementation Roadmap

Phase 1: Foundation (Weeks 1-4)

Phase 2: Scaling and Improvement (Weeks 5-8)

Phase 3: Production Deployment (Weeks 9-12)

Phase 4: Advanced Features (Month 4+)

💡 Success Metrics

Technical Metrics

User Experience Metrics

🎯 Getting Started

# 1. Setup environment
pip install pixeltable transformers peft datasets rdflib pyoxigraph

# 2. Initialize Pixeltable schema
python setup_corpus_analysis_schema.py

# 3. Collect training data
python collect_training_data.py --domain medical_forums --num_examples 20

# 4. Train model
python train_corpus_model.py --run_name "corpus_mvp_v1" --epochs 2

# 5. Test on sample corpus
python test_corpus_analysis.py --model_path "./models/corpus_mvp_v1"

Expected First Results

This specification provides a complete roadmap for building corpus-driven ontology discovery using real document analysis rather than theoretical domain knowledge. 🎯

OntologyLM: Corpus-Driven Schema Discovery System
by @goodlux with claude-sonnet-4-5-20250929
2026-01-19T08:24:08+00:00