Extending Metadata with LLMs

Raw data rarely tells the complete story. Even well-structured datasets often lack context, connections, and enrichment that would make them truly valuable. Large Language Models (LLMs) offer a powerful solution for extending metadata in ways that were previously too labor-intensive or technically complex. I touched on extending data in a another post but let's explore it more deeply here.

The Metadata Limitation Problem

Most datasets contain only the metadata that was easily captured during collection. This often means:

• Basic timestamps and identifiers
• Source information
• Simple categorization
• Explicitly tagged elements

What's missing is the rich contextual information that human analysts intuitively understand but isn't explicitly recorded. This is where LLMs can transform your data's utility.

Key Metadata Enrichments Possible with LLMs

Entity Recognition and Linking

LLMs excel at identifying entities mentioned in textual data:

Original text: "The meeting in Frankfurt with Müller AG and representatives from the Ministry went well."

LLM-enhanced metadata:
{
  "entities": {
    "locations": ["Frankfurt", "Germany"],
    "organizations": ["Müller AG", "Ministry"],
    "event_type": "business meeting",
    "entity_relationships": [
      {"entity": "Müller AG", "type": "company", "industry": "manufacturing", "headquarters": "Munich"},
      {"entity": "Ministry", "likely_refers_to": "German Federal Ministry of Economics", "type": "government"}
    ]
  }
}

Geographic Enrichment

LLMs can identify and normalize location information:

Original metadata: {"location": "SF"}

LLM-enhanced metadata:
{
  "location": {
    "original_text": "SF",
    "normalized": "San Francisco",
    "country": "United States",
    "state": "California",
    "coordinates": {"lat": 37.7749, "long": -122.4194},
    "timezone": "America/Los_Angeles",
    "type": "city"
  }
}

Temporal Context

LLMs can add time-based context to your data:

Original metadata: {"date": "2023-11-16"}

LLM-enhanced metadata:
{
  "date": "2023-11-16",
  "day_of_week": "Thursday",
  "quarter": "Q4",
  "fiscal_year": "FY2023",
  "holiday_context": "One week before US Thanksgiving",
  "business_day": true,
  "season": {
    "northern_hemisphere": "Autumn",
    "southern_hemisphere": "Spring"
  }
}

Domain-Specific Classification

LLMs can apply specialized categorization schemas to general content:

Original content: "Patient reports increasing pain in the lower right quadrant, with fever and nausea. Pain increases with movement."

LLM-enhanced medical metadata:
{
  "likely_conditions": ["Appendicitis", "Kidney infection", "Ovarian cyst"],
  "symptoms": ["Abdominal pain", "Fever", "Nausea", "Pain on movement"],
  "anatomical_location": "Lower right quadrant abdomen",
  "severity_indicators": ["Increasing pain", "Movement exacerbation"],
  "triage_category": "Urgent",
  "recommended_diagnostics": ["Complete blood count", "Abdominal CT scan", "Urinalysis"]
}

Language Translation and Normalization

LLMs can bridge language barriers in your data:

Original text: "Der Patient klagt über Schmerzen im rechten Arm nach einem Sturz."

LLM-enhanced metadata:
{
  "original_language": "German",
  "translation": {
    "english": "The patient complains of pain in the right arm after a fall.",
    "spanish": "El paciente se queja de dolor en el brazo derecho después de una caída.",
    "french": "Le patient se plaint de douleurs au bras droit après une chute."
  },
  "medical_terminology": {
    "normalized": "Patient presents with pain in right upper extremity following trauma (fall).",
    "ICD-10": "S40.9, W19"
  }
}

Sentiment and Emotional Context

LLMs can extract emotional signals from textual data:

Original customer feedback: "Your product finally solved our problem after trying everything else."

LLM-enhanced metadata:
{
  "sentiment": "Positive",
  "sentiment_score": 0.87,
  "emotional_states": ["Relief", "Satisfaction"],
  "implied_history": "Multiple previous failed solutions",
  "customer_journey_stage": "Resolution",
  "loyalty_indicators": ["Persistence through challenges", "Successful resolution"],
  "follow_up_priority": "Medium-high - successful but indicates previous difficulties"
}

Topical Extraction and Summarization

LLMs can identify key topics and generate concise summaries:

Original text: [Long document about market trends]

LLM-enhanced metadata:
{
  "main_topics": ["Semiconductor supply chain", "Electric vehicle manufacturing", "Asian market expansion"],
  "key_statistics": [
    {"value": "27% growth", "context": "EV production in Southeast Asia"},
    {"value": "8-month delay", "context": "Average semiconductor procurement timeline"}
  ],
  "executive_summary": "Global semiconductor shortages continue to impact EV manufacturing, with Asian markets showing resilience through alternative sourcing strategies. Supply chain diversification becoming critical success factor.",
  "related_industries": ["Automotive", "Electronics", "Manufacturing", "Logistics"],
  "key_entities": ["TSMC", "BYD", "Global Foundries", "Tesla Shanghai"]
}

Relationship Mapping

LLMs can identify how different entities relate to each other:

Original data: [Employee communications dataset]

LLM-enhanced metadata:
{
  "organizational_structure": {
    "reporting_relationships": [
      {"manager": "Sarah Chen", "direct_reports": ["Alex Johnson", "Raj Patel", "Maria Rodriguez"]},
      {"manager": "David Kim", "direct_reports": ["Lisa Wong", "James Smith"]}
    ],
    "informal_influence": [
      {"influencer": "Raj Patel", "influenced_domains": ["Technical decisions", "Hiring processes"]},
      {"influencer": "Lisa Wong", "influenced_domains": ["Project prioritization", "Customer relationships"]}
    ],
    "team_collaborations": [
      {"frequent_collaborators": ["Sarah Chen", "David Kim", "Lisa Wong"], "context": "Strategic planning"},
      {"frequent_collaborators": ["Alex Johnson", "Maria Rodriguez"], "context": "Customer implementation"}
    ]
  }
}

Compliance and Risk Annotation

LLMs can identify potentially sensitive information:

Original data: [Customer support transcript]

LLM-enhanced metadata:
{
  "sensitive_data_detected": {
    "PII": ["email address", "phone number"],
    "financial": ["credit card number (partial)"],
    "applicable_regulations": ["GDPR", "PCI DSS"]
  },
  "risk_factors": {
    "data_handling": "Customer service agent requested full credit card number",
    "compliance_violations": "Agent stored customer information in personal notes"
  },
  "remediation_required": true,
  "remediation_actions": ["Agent training", "Transcript redaction", "Process review"]
}

Implementation Approach

To effectively extend your dataset metadata with LLMs:

1. Analyze your current metadata gaps - Identify what contextual information would add the most value
2. Develop targeted prompts - Create LLM prompts specifically designed to extract the desired metadata
3. Process batches efficiently - Set up workflows to process data in appropriately sized batches
4. Establish verification mechanisms - Implement confidence scoring and sampling for quality control
5. Create metadata storage - Design appropriate data structures for the enhanced metadata
6. Build feedback loops - Continually improve extraction based on accuracy assessments

Technical Example: Basic Implementation

Here's a simplified code approach for metadata extraction:

import json
from openai import OpenAI

client = OpenAI()

def enhance_metadata(text, original_metadata=None):
    # Construct prompt with the text and any existing metadata
    prompt = f"""
    Analyze the following text and existing metadata to extract extended metadata.
    Focus on entities, locations, sentiment, key topics, and relationships.
    
    TEXT:
    {text}
    
    EXISTING METADATA:
    {json.dumps(original_metadata) if original_metadata else "None"}
    
    Provide extended metadata in JSON format.
    """
    
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[
            {"role": "system", "content": "You extract structured metadata from text."},
            {"role": "user", "content": prompt}
        ],
        response_format={"type": "json_object"}
    )
    
    return json.loads(response.choices[0].message.content)

# Example usage
text = "The quarterly meeting in Boston with Amazon representatives discussed cloud integration challenges for the healthcare sector."
original_metadata = {"date": "2023-10-15", "document_type": "meeting_notes"}

enhanced = enhance_metadata(text, original_metadata)
print(json.dumps(enhanced, indent=2))

Practical Applications

This approach has proven particularly valuable in:

Intelligence Analysis

Analysts use LLM-enhanced metadata to identify connections between seemingly unrelated reports, extracting entities, locations, and events to build comprehensive intelligence pictures.

Content Management

Media organizations enrich their content libraries with detailed metadata that enables precise content discovery and reuse, even when the original tagging was minimal.

Research Datasets

Academic researchers use LLMs to standardize and enrich datasets from multiple sources, creating common frames of reference that make disparate data comparable.

Data Governance

Organizations identify sensitive information across unstructured data, automatically generating metadata that aids in compliance efforts.

Limitations and Considerations

When implementing LLM-based metadata enrichment:

• Verify accuracy - LLMs can occasionally "hallucinate" or infer incorrect information
• Maintain provenance - Clearly distinguish original from LLM-generated metadata
• Consider bias - Be aware that LLMs may reproduce biases present in their training data
• Optimize processing - For large datasets, batching and parallelization are essential

Conclusion

LLMs offer a transformative approach to metadata enrichment, making it possible to extract context, relationships, and insights that would be prohibitively expensive to generate manually. By thoughtfully applying these techniques, organizations can dramatically increase the value and utility of their existing data assets without requiring changes to core collection processes.

The key is to start with clear objectives about what additional context would most benefit your specific use cases, then design targeted enrichment strategies that address those needs.