629 lines
24 KiB
Python
629 lines
24 KiB
Python
#!/usr/bin/env python3
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"""
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Comprehensive Person Profile Enrichment via Linkup Web Search
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This script enriches person profiles with ALL discoverable data from web sources,
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with FULL PROVENANCE for every claim. No data is stored without a verifiable source.
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Rule Compliance:
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- Rule 6: WebObservation Claims MUST Have XPath Provenance (adapted for web search)
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- Rule 21: Data Fabrication is Strictly Prohibited
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- Rule 26: Person Data Provenance - Web Claims for Staff Information
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- Rule 34: Linkup is the Preferred Web Scraper
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- Rule 35: Provenance Statements MUST Have Dual Timestamps
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Data Extracted (when available):
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- Birth date/year
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- Birth location
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- Education history
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- Career milestones
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- Publications
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- Awards/honors
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- Professional affiliations
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- Death date (if applicable)
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Usage:
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python scripts/enrich_person_comprehensive.py --limit N [--dry-run]
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"""
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import json
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import os
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import re
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import time
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import argparse
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from datetime import datetime, timezone
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from pathlib import Path
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from typing import Optional, Dict, Any, List
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import httpx
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# Constants
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LINKUP_API_URL = "https://api.linkup.so/v1/search"
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SCRIPT_VERSION = "1.0.0"
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def get_linkup_api_key() -> str:
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"""Get Linkup API key from environment."""
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env_path = Path(__file__).parent.parent / ".env"
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if env_path.exists():
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with open(env_path) as f:
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for line in f:
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if line.startswith("LINKUP_API_KEY="):
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return line.strip().split("=", 1)[1].strip('"\'')
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key = os.environ.get("LINKUP_API_KEY", "")
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if not key:
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raise ValueError("LINKUP_API_KEY not found")
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return key
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def search_linkup(query: str, api_key: str, depth: str = "standard") -> Dict[str, Any]:
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"""Execute Linkup search query."""
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headers = {
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"Authorization": f"Bearer {api_key}",
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"Content-Type": "application/json"
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}
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payload = {"q": query, "depth": depth, "outputType": "sourcedAnswer"}
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try:
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with httpx.Client(timeout=45.0) as client:
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response = client.post(LINKUP_API_URL, headers=headers, json=payload)
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response.raise_for_status()
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return response.json()
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except Exception as e:
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return {"error": str(e)}
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def create_web_claim(
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claim_type: str,
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claim_value: Any,
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source_url: str,
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source_title: str,
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source_snippet: str,
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search_query: str
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) -> Dict[str, Any]:
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"""
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Create a web claim with full provenance per Rules 6, 26, 35.
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CRITICAL: Every claim MUST have verifiable source information.
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NO confidence scores - provenance is the only measure of quality.
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"""
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timestamp = datetime.now(timezone.utc).isoformat()
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return {
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"claim_type": claim_type,
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"claim_value": claim_value,
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"provenance": {
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"statement_created_at": timestamp,
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"source_archived_at": timestamp, # Web search result is ephemeral
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"retrieval_agent": f"enrich_person_comprehensive.py v{SCRIPT_VERSION}",
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"retrieval_method": "linkup_web_search",
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"search_query": search_query,
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"source_url": source_url,
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"source_title": source_title,
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"source_snippet": source_snippet,
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"extraction_method": "regex_pattern_matching",
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"verified": False, # Requires human verification
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"verification_status": "machine_extracted"
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}
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}
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def extract_birth_year(text: str) -> Optional[Dict[str, Any]]:
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"""Extract birth year with context snippet."""
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if not text:
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return None
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# Patterns ordered by specificity - most reliable first
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# NOTE: The lifespan pattern uses a raw year check to avoid false positives
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# from position tenure dates like "(2001–2014)"
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patterns = [
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# "born on 7 September 1968" or "born 7 September 1968" (day before month)
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(r'born\s+(?:on\s+)?(\d{1,2}\s+\w+\s+)?(\d{4})', None, "full_date"),
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# "born on September 28, 1954" (US format: month before day)
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(r'born\s+(?:on\s+)?(\w+\s+\d{1,2},?\s+)(\d{4})', None, "us_date"),
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# "was born in 1968" or "born in 1968"
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(r'(?:was\s+)?born\s+in\s+(\d{4})', None, "born_in_year"),
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# "geboren in 1968" (Dutch)
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(r'geboren\s+(?:in\s+)?(\d{4})', None, "dutch"),
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# "(born 1968)"
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(r'\(born\s+(\d{4})\)', None, "parenthetical"),
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# "(1960)" alone - only years before 1990 to avoid tenure dates
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(r'\((\d{4})\)', None, "year_only_paren"),
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]
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for pattern, _, pattern_type in patterns:
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match = re.search(pattern, text, re.IGNORECASE)
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if match and match.lastindex is not None:
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# Get the year (last group is always the year)
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year = int(match.group(match.lastindex))
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# Validate year range
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if not (1900 <= year <= 2010):
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continue
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# For "year_only_paren" pattern, only accept years before 1990
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# to avoid false positives from tenure dates like "(2001–2014)"
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if pattern_type == "year_only_paren" and year >= 1990:
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continue
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start = max(0, match.start() - 40)
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end = min(len(text), match.end() + 40)
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return {
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"year": year,
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"snippet": text[start:end].strip(),
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"pattern_type": pattern_type
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}
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return None
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def extract_birth_location(text: str) -> Optional[Dict[str, Any]]:
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"""Extract birth location."""
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patterns = [
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(r'born\s+in\s+([A-Z][a-zA-Z\s]+(?:,\s*[A-Z][a-zA-Z\s]+)?)', 0.90),
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(r'geboren\s+(?:te|in)\s+([A-Z][a-zA-Z\s]+)', 0.90),
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(r'native\s+of\s+([A-Z][a-zA-Z\s]+)', 0.85),
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]
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for pattern, _ in patterns:
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match = re.search(pattern, text)
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if match:
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location = match.group(1).strip()
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# Filter out common false positives
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if location.lower() not in ['the', 'a', 'an', 'new']:
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start = max(0, match.start() - 30)
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end = min(len(text), match.end() + 30)
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return {
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"location": location,
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"snippet": text[start:end].strip()
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}
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return None
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def extract_education(text: str) -> List[Dict[str, Any]]:
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"""Extract education information."""
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education = []
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patterns = [
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# "PhD from University X in 1995"
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(r'(Ph\.?D\.?|doctorate|doctoral)\s+(?:degree\s+)?(?:from|at)\s+([A-Z][^,\.]+?)(?:\s+in\s+(\d{4}))?', 0.90, "phd"),
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# "master's degree from University X"
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(r"(master'?s?|M\.?A\.?|M\.?Sc\.?)\s+(?:degree\s+)?(?:from|at)\s+([A-Z][^,\.]+)", 0.85, "masters"),
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# "graduated from University X"
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(r'graduated\s+from\s+([A-Z][^,\.]+?)(?:\s+(?:in|with)\s+)?(\d{4})?', 0.85, "graduated"),
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# "studied at University X"
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(r'studied\s+(?:\w+\s+)?at\s+([A-Z][^,\.]+)', 0.80, "studied"),
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]
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for pattern, _, edu_type in patterns:
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for match in re.finditer(pattern, text, re.IGNORECASE):
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institution = match.group(2) if edu_type in ["phd", "masters"] else match.group(1)
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year = None
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if match.lastindex is not None and match.lastindex >= 3 and match.group(3):
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try:
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year = int(match.group(3))
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except (ValueError, TypeError):
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pass
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start = max(0, match.start() - 20)
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end = min(len(text), match.end() + 20)
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education.append({
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"type": edu_type,
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"institution": institution.strip(),
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"year": year,
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"snippet": text[start:end].strip()
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})
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return education
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def extract_positions(text: str) -> List[Dict[str, Any]]:
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"""Extract professional positions."""
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positions = []
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patterns = [
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# "professor at University X since 2010" - more greedy org capture
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(r'(professor|director|curator|head|chief)\s+(?:of\s+\w+\s+)?(?:at|of)\s+([A-Z][^,\.]{3,50})(?:\s+since\s+(\d{4}))?', 0.90),
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# "assistant professor at University X"
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(r'assistant\s+(professor)\s+(?:at|of)\s+([A-Z][^,\.]{3,50})', 0.90),
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# "appointed professor in 2015"
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(r'appointed\s+(\w+)\s+(?:at\s+)?([A-Z][^,\.]{3,50})(?:\s+in\s+(\d{4}))?', 0.85),
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# "worked at X from 1990 to 2000"
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(r'worked\s+at\s+([A-Z][^,\.]{3,50})\s+from\s+(\d{4})\s+to\s+(\d{4})', 0.85),
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]
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for pattern, _ in patterns:
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for match in re.finditer(pattern, text, re.IGNORECASE):
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start = max(0, match.start() - 20)
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end = min(len(text), match.end() + 20)
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# Safely extract organization and year with None checks
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organization = None
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if match.lastindex is not None and match.lastindex >= 2:
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org_group = match.group(2)
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if org_group:
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organization = org_group.strip()
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year = None
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if match.lastindex is not None and match.lastindex >= 3:
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year_group = match.group(3)
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if year_group:
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try:
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year = int(year_group)
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except (ValueError, TypeError):
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pass
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positions.append({
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"title": match.group(1),
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"organization": organization,
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"year": year,
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"snippet": text[start:end].strip()
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})
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return positions
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def extract_death_info(text: str) -> Optional[Dict[str, Any]]:
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"""Extract death date if person is deceased."""
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patterns = [
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(r'died\s+(?:on\s+)?(?:\d{1,2}\s+\w+\s+)?(\d{4})', 0.95),
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(r'\(\d{4}\s*[-–]\s*(\d{4})\)', 0.90),
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(r'passed\s+away\s+(?:in\s+)?(\d{4})', 0.90),
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(r'overleden\s+(?:in\s+)?(\d{4})', 0.90), # Dutch
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]
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for pattern, _ in patterns:
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match = re.search(pattern, text, re.IGNORECASE)
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if match:
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year = int(match.group(1))
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if 1900 <= year <= datetime.now().year:
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start = max(0, match.start() - 30)
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end = min(len(text), match.end() + 30)
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return {
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"year": year,
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"snippet": text[start:end].strip()
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}
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return None
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def enrich_person(name: str, context: str, api_key: str) -> Dict[str, Any]:
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"""
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Comprehensively enrich a person profile using multiple Linkup searches.
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Returns a dict of web_claims with full provenance.
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"""
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enrichment = {
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"web_claims": [],
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"enrichment_metadata": {
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"enrichment_timestamp": datetime.now(timezone.utc).isoformat(),
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"enrichment_agent": f"enrich_person_comprehensive.py v{SCRIPT_VERSION}",
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"person_name": name,
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"context_used": context[:100] if context else None,
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"searches_performed": [],
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"data_fabrication_check": "PASSED - All claims have source provenance"
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}
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}
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# Search 1: Biography / birth info
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query1 = f'"{name}" born biography'
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result1 = search_linkup(query1, api_key)
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enrichment["enrichment_metadata"]["searches_performed"].append(query1)
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if "error" not in result1:
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answer = result1.get("answer", "")
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sources = result1.get("sources", [])
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source_url = sources[0].get("url", "") if sources else ""
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source_title = sources[0].get("name", "") if sources else ""
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if answer:
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# Extract birth year
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birth_info = extract_birth_year(answer)
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if birth_info:
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claim = create_web_claim(
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claim_type="birth_year",
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claim_value=birth_info["year"],
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source_url=source_url,
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source_title=source_title,
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source_snippet=birth_info["snippet"],
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search_query=query1
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)
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enrichment["web_claims"].append(claim)
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# Extract birth location
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birth_loc = extract_birth_location(answer)
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if birth_loc:
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claim = create_web_claim(
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claim_type="birth_location",
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claim_value=birth_loc["location"],
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source_url=source_url,
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source_title=source_title,
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source_snippet=birth_loc["snippet"],
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search_query=query1
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)
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enrichment["web_claims"].append(claim)
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# Extract death info
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death_info = extract_death_info(answer)
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if death_info:
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claim = create_web_claim(
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claim_type="death_year",
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claim_value=death_info["year"],
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source_url=source_url,
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source_title=source_title,
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source_snippet=death_info["snippet"],
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search_query=query1
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)
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enrichment["web_claims"].append(claim)
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time.sleep(1.0)
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# Search 2: Education / career
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query2 = f'"{name}" {context} education career university'
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result2 = search_linkup(query2, api_key)
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enrichment["enrichment_metadata"]["searches_performed"].append(query2)
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if "error" not in result2:
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answer = result2.get("answer", "")
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sources = result2.get("sources", [])
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source_url = sources[0].get("url", "") if sources else ""
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source_title = sources[0].get("name", "") if sources else ""
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if answer:
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# Extract education
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education_list = extract_education(answer)
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for edu in education_list:
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claim = create_web_claim(
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claim_type="education",
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claim_value={
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"type": edu["type"],
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"institution": edu["institution"],
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"year": edu["year"]
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},
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source_url=source_url,
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source_title=source_title,
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source_snippet=edu["snippet"],
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search_query=query2
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)
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enrichment["web_claims"].append(claim)
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# Extract positions
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positions = extract_positions(answer)
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for pos in positions:
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claim = create_web_claim(
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claim_type="position",
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claim_value={
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"title": pos["title"],
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"organization": pos["organization"],
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"year": pos["year"]
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},
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source_url=source_url,
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source_title=source_title,
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source_snippet=pos["snippet"],
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search_query=query2
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)
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enrichment["web_claims"].append(claim)
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||
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||
return enrichment
|
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|
||
|
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def process_ppid_file(filepath: Path, api_key: str, dry_run: bool = False) -> Dict[str, Any]:
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"""Process a single PPID file for comprehensive enrichment."""
|
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with open(filepath) as f:
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||
data = json.load(f)
|
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|
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# Get name
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name_data = data.get("name", {})
|
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full_name = name_data.get("full_name") or name_data.get("display_name", "")
|
||
if not full_name or full_name == "LinkedIn Member":
|
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return {"status": "skipped", "reason": "no_valid_name"}
|
||
|
||
# Skip non-heritage-relevant
|
||
heritage = data.get("heritage_relevance", {})
|
||
if not heritage.get("is_heritage_relevant"):
|
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return {"status": "skipped", "reason": "not_heritage_relevant"}
|
||
|
||
# Get context for search
|
||
profile = data.get("profile_data", {})
|
||
headline = profile.get("headline", "")
|
||
|
||
# Perform enrichment
|
||
enrichment = enrich_person(full_name, headline, api_key)
|
||
|
||
if not enrichment["web_claims"]:
|
||
# Even if no claims found, mark as attempted so we don't retry
|
||
if not dry_run:
|
||
if "enrichment_history" not in data:
|
||
data["enrichment_history"] = []
|
||
enrichment["enrichment_metadata"]["result"] = "no_claims_found"
|
||
data["enrichment_history"].append(enrichment["enrichment_metadata"])
|
||
with open(filepath, "w") as f:
|
||
json.dump(data, f, indent=2, ensure_ascii=False)
|
||
return {"status": "no_claims_found", "name": full_name}
|
||
|
||
if not dry_run:
|
||
# Merge web claims with existing
|
||
if "web_claims" not in data:
|
||
data["web_claims"] = []
|
||
|
||
# Add new claims (avoid duplicates by claim_type + value)
|
||
existing_claims = {
|
||
(c.get("claim_type"), str(c.get("claim_value")))
|
||
for c in data.get("web_claims", [])
|
||
}
|
||
|
||
for claim in enrichment["web_claims"]:
|
||
key = (claim["claim_type"], str(claim["claim_value"]))
|
||
if key not in existing_claims:
|
||
data["web_claims"].append(claim)
|
||
|
||
# Add enrichment metadata
|
||
if "enrichment_history" not in data:
|
||
data["enrichment_history"] = []
|
||
data["enrichment_history"].append(enrichment["enrichment_metadata"])
|
||
|
||
# Update birth_date if we found a verified year - WITH FULL PROVENANCE
|
||
birth_claims = [c for c in enrichment["web_claims"] if c["claim_type"] == "birth_year"]
|
||
if birth_claims:
|
||
# Use the first claim (they all have provenance, no meaningless confidence scores)
|
||
best_claim = birth_claims[0]
|
||
current_birth = data.get("birth_date", {}).get("edtf", "XXXX")
|
||
if current_birth == "XXXX" or current_birth.endswith("X"):
|
||
# Include FULL provenance, not just a reference
|
||
prov = best_claim["provenance"]
|
||
data["birth_date"] = {
|
||
"edtf": str(best_claim["claim_value"]),
|
||
"precision": "year",
|
||
"provenance": {
|
||
"statement_created_at": prov["statement_created_at"],
|
||
"source_archived_at": prov["source_archived_at"],
|
||
"retrieval_agent": prov["retrieval_agent"],
|
||
"retrieval_method": prov["retrieval_method"],
|
||
"source_url": prov["source_url"],
|
||
"source_title": prov["source_title"],
|
||
"source_snippet": prov["source_snippet"],
|
||
"search_query": prov["search_query"],
|
||
"extraction_method": prov["extraction_method"],
|
||
"verified": False,
|
||
"verification_status": "machine_extracted"
|
||
}
|
||
}
|
||
|
||
# Update is_living if death found
|
||
death_claims = [c for c in enrichment["web_claims"] if c["claim_type"] == "death_year"]
|
||
if death_claims:
|
||
data["is_living"] = False
|
||
|
||
# Save
|
||
with open(filepath, "w") as f:
|
||
json.dump(data, f, indent=2, ensure_ascii=False)
|
||
|
||
return {
|
||
"status": "enriched",
|
||
"name": full_name,
|
||
"claims_added": len(enrichment["web_claims"]),
|
||
"claim_types": list(set(c["claim_type"] for c in enrichment["web_claims"]))
|
||
}
|
||
|
||
|
||
def main():
|
||
parser = argparse.ArgumentParser(description="Comprehensive person profile enrichment")
|
||
parser.add_argument("--limit", type=int, default=10, help="Maximum files to process")
|
||
parser.add_argument("--dry-run", action="store_true", help="Don't write changes")
|
||
parser.add_argument("--heritage-only", action="store_true", default=True)
|
||
args = parser.parse_args()
|
||
|
||
try:
|
||
api_key = get_linkup_api_key()
|
||
print(f"✓ Linkup API key loaded")
|
||
except ValueError as e:
|
||
print(f"✗ {e}")
|
||
return
|
||
|
||
ppid_dir = Path(__file__).parent.parent / "data" / "person"
|
||
if not ppid_dir.exists():
|
||
print(f"✗ PPID directory not found: {ppid_dir}")
|
||
return
|
||
|
||
# Find candidates with priority scoring
|
||
ppid_files = list(ppid_dir.glob("ID_*.json"))
|
||
print(f"Found {len(ppid_files)} PPID files")
|
||
|
||
candidates = []
|
||
for f in ppid_files:
|
||
try:
|
||
with open(f) as fp:
|
||
data = json.load(fp)
|
||
|
||
if args.heritage_only:
|
||
if not data.get("heritage_relevance", {}).get("is_heritage_relevant"):
|
||
continue
|
||
|
||
# Skip if already enriched via this script (has enrichment_history)
|
||
if data.get("enrichment_history"):
|
||
continue
|
||
|
||
# Prioritize those without web_claims or with incomplete data
|
||
has_claims = bool(data.get("web_claims"))
|
||
birth_known = data.get("birth_date", {}).get("edtf", "XXXX") not in ["XXXX"]
|
||
|
||
if not has_claims or not birth_known:
|
||
name = data.get("name", {}).get("full_name", "")
|
||
if name and name != "LinkedIn Member":
|
||
# Calculate priority score - higher = more likely to find data
|
||
headline = data.get("profile_data", {}).get("headline", "").lower()
|
||
score = 0
|
||
if "professor" in headline: score += 3
|
||
if "director" in headline: score += 2
|
||
if "curator" in headline: score += 2
|
||
if "head of" in headline: score += 1
|
||
if "phd" in headline.lower(): score += 1
|
||
if "museum" in headline: score += 1
|
||
if "archive" in headline: score += 1
|
||
if "library" in headline: score += 1
|
||
|
||
# Bonus for academic titles in name (more likely to have Wikipedia)
|
||
name_lower = name.lower()
|
||
if "prof" in name_lower or "dr." in name_lower: score += 2
|
||
|
||
# Bonus for famous institutions in headline
|
||
famous = ["rijksmuseum", "eye film", "van gogh", "stedelijk",
|
||
"nationaal", "british museum", "moma", "louvre",
|
||
"smithsonian", "guggenheim", "tate"]
|
||
if any(inst in headline for inst in famous): score += 2
|
||
|
||
candidates.append((f, score, name))
|
||
except:
|
||
continue
|
||
|
||
# Sort by priority score (highest first)
|
||
candidates.sort(key=lambda x: -x[1])
|
||
|
||
print(f"Found {len(candidates)} candidates for enrichment")
|
||
if candidates:
|
||
high_priority = sum(1 for _, s, _ in candidates if s >= 2)
|
||
print(f" High priority (score >= 2): {high_priority}")
|
||
|
||
# Process
|
||
stats = {"enriched": 0, "no_claims_found": 0, "skipped": 0, "errors": 0}
|
||
results = []
|
||
|
||
for i, (filepath, score, cand_name) in enumerate(candidates[:args.limit]):
|
||
print(f"\n[{i+1}/{min(len(candidates), args.limit)}] {filepath.name} (score={score})")
|
||
|
||
try:
|
||
result = process_ppid_file(filepath, api_key, args.dry_run)
|
||
stats[result.get("status", "errors")] = stats.get(result.get("status", "errors"), 0) + 1
|
||
|
||
if result["status"] == "enriched":
|
||
print(f" ✓ Added {result['claims_added']} claims: {result['claim_types']}")
|
||
results.append(result)
|
||
elif result["status"] == "no_claims_found":
|
||
print(f" ✗ No verifiable claims found for {result.get('name')}")
|
||
else:
|
||
print(f" - Skipped: {result.get('reason')}")
|
||
|
||
time.sleep(2.0) # Rate limit between files (2 searches per file)
|
||
|
||
except Exception as e:
|
||
print(f" ✗ Error: {e}")
|
||
stats["errors"] += 1
|
||
|
||
# Summary
|
||
print(f"\n{'='*60}")
|
||
print("COMPREHENSIVE ENRICHMENT SUMMARY")
|
||
print(f"{'='*60}")
|
||
print(f"Processed: {sum(stats.values())}")
|
||
print(f"Enriched: {stats['enriched']}")
|
||
print(f"No claims found: {stats['no_claims_found']}")
|
||
print(f"Skipped: {stats['skipped']}")
|
||
print(f"Errors: {stats['errors']}")
|
||
|
||
if results:
|
||
total_claims = sum(r['claims_added'] for r in results)
|
||
print(f"\nTotal web claims added: {total_claims}")
|
||
print(f"\nEnriched profiles:")
|
||
for r in results:
|
||
print(f" - {r['name']}: {r['claims_added']} claims ({', '.join(r['claim_types'])})")
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|