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GLAMORCUBEPSXHF Multilingual Vocabulary - Design Patterns

Version: 1.0
Date: 2025-11-11
Status: Planning Phase (Phase 0)
Dependencies: 01-architecture.md, 00-MASTER_CHECKLIST.md

Overview
Multilingual Data Patterns
Script Handling Patterns
Matching and Disambiguation Patterns
Error Handling Patterns
Testing Patterns
Data Validation Patterns
Provenance Tracking Patterns

Overview

This document defines reusable design patterns for building the GLAMORCUBEPSXHF multilingual vocabulary system. These patterns address common challenges in multilingual NLP, Unicode handling, fuzzy matching, and semantic disambiguation.

Design Principles

Language Neutrality: No hardcoded language assumptions (avoid "English-first" bias)
Script Awareness: Treat Latin, Cyrillic, Arabic, CJK, etc. as first-class citizens
Provenance First: Every term carries source metadata for verification
Fail Gracefully: Degrade to fallback behavior rather than crashing
Test-Driven: Validate patterns with real multilingual test cases

Multilingual Data Patterns

Pattern 1: Language-Tagged Text

Problem: Store terms with their language codes without losing metadata

Solution: Use BCP 47 language tags with ISO 639-3 codes

from dataclasses import dataclass
from typing import Optional

@dataclass
class LanguageTaggedText:
    """Text with explicit language tagging."""
    text: str
    language: str  # ISO 639-3 code (e.g., "eng", "ara", "ind")
    script: Optional[str] = None  # ISO 15924 code (e.g., "Latn", "Arab", "Hans")
    region: Optional[str] = None  # ISO 3166-1 alpha-2 (e.g., "US", "SA", "ID")
    
    @property
    def bcp47_tag(self) -> str:
        """Generate BCP 47 language tag."""
        tag = self.language
        if self.script:
            tag += f"-{self.script}"
        if self.region:
            tag += f"-{self.region}"
        return tag
    
    def __repr__(self):
        return f'"{self.text}"@{self.bcp47_tag}'

# Example usage
terms = [
    LanguageTaggedText("temple", "eng", "Latn"),
    LanguageTaggedText("معبد", "ara", "Arab"),
    LanguageTaggedText("寺", "jpn", "Hani"),  # Japanese kanji
    LanguageTaggedText("keramat", "ind", "Latn", "ID"),  # Indonesian in Indonesia
    LanguageTaggedText("kramat", "msa", "Latn", "MY"),  # Malay in Malaysia
]

for term in terms:
    print(term)  # "temple"@eng-Latn, "معبد"@ara-Arab, etc.

Benefits:

✅ Unambiguous language identification
✅ Supports regional variants (Indonesian vs. Malaysian Malay)
✅ Script-aware (Serbian Cyrillic vs. Latin)
✅ Compatible with RDF language tags ("معبد"@ar)

When to Use:

Storing terms in JSON/YAML
RDF/JSON-LD serialization
Multilingual UI display

Pattern 2: Multilingual Dictionary

Problem: Efficiently look up terms across multiple languages

Solution: Use nested dictionaries with language keys

from typing import Dict, List, Optional
from collections import defaultdict

class MultilingualDictionary:
    """Dictionary supporting multilingual key-value lookups."""
    
    def __init__(self):
        # Structure: {language: {normalized_term: [metadata]}}
        self.data: Dict[str, Dict[str, List[Dict]]] = defaultdict(lambda: defaultdict(list))
    
    def add_term(
        self,
        term: str,
        language: str,
        metadata: Dict
    ):
        """Add a term with metadata."""
        normalized = self._normalize(term, language)
        self.data[language][normalized].append(metadata)
    
    def lookup(
        self,
        term: str,
        language: str
    ) -> List[Dict]:
        """Look up term in specific language."""
        normalized = self._normalize(term, language)
        return self.data[language].get(normalized, [])
    
    def lookup_all_languages(
        self,
        term: str
    ) -> Dict[str, List[Dict]]:
        """Look up term across all languages."""
        results = {}
        for lang in self.data:
            matches = self.lookup(term, lang)
            if matches:
                results[lang] = matches
        return results
    
    def _normalize(self, term: str, language: str) -> str:
        """Language-aware normalization."""
        import unicodedata
        
        # NFC normalization (canonical composition)
        normalized = unicodedata.normalize('NFC', term)
        
        # Lowercase only for case-insensitive scripts
        if self._is_case_insensitive_script(language):
            normalized = normalized.lower()
        
        return normalized
    
    def _is_case_insensitive_script(self, language: str) -> bool:
        """Check if language uses case-insensitive script."""
        # Latin, Cyrillic, Greek are case-insensitive
        # Arabic, Hebrew, CJK are case-sensitive (no case distinction)
        case_insensitive_langs = {'eng', 'fra', 'deu', 'rus', 'pol', 'ces', ...}
        return language in case_insensitive_langs

# Example usage
vocab = MultilingualDictionary()

vocab.add_term("temple", "eng", {
    "qid": "Q44539",
    "class": "H",
    "confidence": 0.95
})

vocab.add_term("معبد", "ara", {
    "qid": "Q44539",
    "class": "H",
    "confidence": 0.95
})

vocab.add_term("keramat", "ind", {
    "class": "H",
    "confidence": 0.75,
    "source": "exa"
})

# Lookup
print(vocab.lookup("temple", "eng"))  # [{qid: Q44539, ...}]
print(vocab.lookup("TEMPLE", "eng"))  # Same (case-insensitive)
print(vocab.lookup("معبد", "ara"))    # [{qid: Q44539, ...}]
print(vocab.lookup_all_languages("keramat"))  # {ind: [{class: H, ...}]}

Benefits:

✅ O(1) lookup per language
✅ Language-aware normalization
✅ Supports multiple matches per term (disambiguation)

When to Use:

In-memory vocabulary cache
Real-time NLP extraction
Testing term coverage

Pattern 3: Translation Equivalence

Problem: Link terms that represent the same concept across languages

Solution: Use Wikidata Q-numbers as universal identifiers

from typing import Dict, List, Set
from dataclasses import dataclass

@dataclass
class ConceptCluster:
    """Group of terms representing the same concept."""
    qid: str  # Wikidata Q-number (universal identifier)
    labels: Dict[str, str]  # language → term
    glamorcubepsxhf_type: str
    confidence: float
    
    def get_translations(self, term: str, source_lang: str) -> Dict[str, str]:
        """Get translations of a term into other languages."""
        if source_lang not in self.labels or self.labels[source_lang] != term:
            return {}
        
        # Return all other languages
        return {lang: label for lang, label in self.labels.items() if lang != source_lang}

# Example: "temple" concept cluster
temple_cluster = ConceptCluster(
    qid="Q44539",
    labels={
        "eng": "temple",
        "fra": "temple",
        "deu": "Tempel",
        "spa": "templo",
        "ara": "معبد",
        "jpn": "寺",
        "zho": "寺庙",
        "hin": "मंदिर",
        "ind": "pura",
        "tha": "วัด"
    },
    glamorcubepsxhf_type="H",
    confidence=0.95
)

# Get translations
translations = temple_cluster.get_translations("temple", "eng")
print(translations)
# {
#   "fra": "temple",
#   "deu": "Tempel",
#   "ara": "معبد",
#   "jpn": "寺",
#   ...
# }

Benefits:

✅ Semantic equivalence via Wikidata
✅ Multilingual synonym detection
✅ Cross-language validation

When to Use:

Building translation dictionaries
Validating extracted terms
Cross-lingual search

Script Handling Patterns

Pattern 4: Script Detection

Problem: Determine which writing system a term uses (Latin, Arabic, CJK, etc.)

Solution: Use Unicode block analysis

import unicodedata
from typing import Dict, Set
from collections import Counter

class ScriptDetector:
    """Detect Unicode script(s) in text."""
    
    # Unicode block ranges for major scripts
    SCRIPT_RANGES = {
        'Latin': [(0x0041, 0x005A), (0x0061, 0x007A), (0x00C0, 0x024F)],
        'Cyrillic': [(0x0400, 0x04FF), (0x0500, 0x052F)],
        'Arabic': [(0x0600, 0x06FF), (0x0750, 0x077F), (0x08A0, 0x08FF)],
        'Devanagari': [(0x0900, 0x097F)],
        'Bengali': [(0x0980, 0x09FF)],
        'Han': [(0x4E00, 0x9FFF), (0x3400, 0x4DBF)],  # CJK Unified Ideographs
        'Hiragana': [(0x3040, 0x309F)],
        'Katakana': [(0x30A0, 0x30FF)],
        'Hangul': [(0xAC00, 0xD7AF)],
        'Thai': [(0x0E00, 0x0E7F)],
        'Hebrew': [(0x0590, 0x05FF)],
        'Greek': [(0x0370, 0x03FF)],
    }
    
    def detect(self, text: str) -> str:
        """Detect dominant script in text."""
        scripts = self.detect_all(text)
        if not scripts:
            return 'Unknown'
        
        # Return most frequent script
        most_common = scripts.most_common(1)[0]
        return most_common[0]
    
    def detect_all(self, text: str) -> Counter:
        """Detect all scripts in text with character counts."""
        script_counts = Counter()
        
        for char in text:
            if char.isspace() or not char.isalnum():
                continue
            
            script = self._char_to_script(char)
            if script:
                script_counts[script] += 1
        
        return script_counts
    
    def _char_to_script(self, char: str) -> str:
        """Map a character to its script."""
        code_point = ord(char)
        
        for script, ranges in self.SCRIPT_RANGES.items():
            for start, end in ranges:
                if start <= code_point <= end:
                    return script
        
        # Fallback to Unicode character name
        try:
            char_name = unicodedata.name(char, '')
            if 'LATIN' in char_name:
                return 'Latin'
            elif 'ARABIC' in char_name:
                return 'Arabic'
            elif 'CJK' in char_name or 'IDEOGRAPH' in char_name:
                return 'Han'
        except ValueError:
            pass
        
        return 'Unknown'
    
    def is_rtl_script(self, script: str) -> bool:
        """Check if script is right-to-left."""
        return script in {'Arabic', 'Hebrew'}

# Example usage
detector = ScriptDetector()

test_terms = [
    "temple",           # Latin
    "معبد",             # Arabic
    "寺",               # Han (CJK)
    "มัสยิด",           # Thai
    "музей",            # Cyrillic
    "museum المتحف",    # Mixed Latin + Arabic
]

for term in test_terms:
    script = detector.detect(term)
    all_scripts = detector.detect_all(term)
    rtl = detector.is_rtl_script(script)
    print(f"{term:20} → {script:15} (RTL: {rtl}) {dict(all_scripts)}")

# Output:
# temple               → Latin           (RTL: False) {'Latin': 6}
# معبد                 → Arabic          (RTL: True) {'Arabic': 4}
# 寺                   → Han             (RTL: False) {'Han': 1}
# มัสยิด               → Thai            (RTL: False) {'Thai': 6}
# музей                → Cyrillic        (RTL: False) {'Cyrillic': 5}
# museum المتحف        → Latin           (RTL: False) {'Latin': 6, 'Arabic': 6}

Benefits:

✅ Accurate script detection for 12+ writing systems
✅ Handles mixed-script text
✅ RTL language detection for UI rendering

When to Use:

Text normalization pipeline
Choosing transliteration algorithm
UI rendering (RTL vs. LTR)

Pattern 5: Unicode Normalization

Problem: Equivalent strings have different byte representations (é vs. e + combining accent)

Solution: Apply NFC normalization consistently

import unicodedata

class UnicodeNormalizer:
    """Normalize Unicode text for consistent comparison."""
    
    def normalize(self, text: str, form: str = 'NFC') -> str:
        """
        Normalize text using Unicode normalization form.
        
        Forms:
        - NFC: Canonical composition (default)
        - NFD: Canonical decomposition
        - NFKC: Compatibility composition
        - NFKD: Compatibility decomposition
        """
        return unicodedata.normalize(form, text)
    
    def are_equivalent(self, text1: str, text2: str) -> bool:
        """Check if two strings are equivalent after normalization."""
        return self.normalize(text1) == self.normalize(text2)

# Example: Vietnamese text with combining diacritics
normalizer = UnicodeNormalizer()

# Two ways to write "Bảo tàng" (museum in Vietnamese)
text1 = "Bảo tàng"  # Precomposed characters (NFC)
text2 = "Bảo tàng"  # Combining diacritics (NFD)

print(f"Byte length: {len(text1.encode('utf-8'))} vs {len(text2.encode('utf-8'))}")
print(f"Equivalent: {normalizer.are_equivalent(text1, text2)}")  # True

# After normalization
norm1 = normalizer.normalize(text1)
norm2 = normalizer.normalize(text2)
print(f"Normalized: '{norm1}' == '{norm2}': {norm1 == norm2}")  # True

Benefits:

✅ Consistent string comparison
✅ Fixes diacritic encoding issues
✅ Database indexing compatibility

When to Use:

Before storing terms in database
Before fuzzy matching
When comparing user input to vocabulary

Pattern 6: Transliteration

Problem: Enable fuzzy matching across scripts (e.g., Arabic "معبد" → "ma'bad")

Solution: Generate Latin transliterations for non-Latin scripts

from typing import Dict, Optional
import unicodedata

class Transliterator:
    """Transliterate non-Latin scripts to Latin alphabet."""
    
    # Simple transliteration tables (production should use ICU or transliterate library)
    ARABIC_TO_LATIN = {
        'ا': 'a', 'ب': 'b', 'ت': 't', 'ث': 'th',
        'ج': 'j', 'ح': 'h', 'خ': 'kh', 'د': 'd',
        'ذ': 'dh', 'ر': 'r', 'ز': 'z', 'س': 's',
        'ش': 'sh', 'ص': 's', 'ض': 'd', 'ط': 't',
        'ظ': 'z', 'ع': "'", 'غ': 'gh', 'ف': 'f',
        'ق': 'q', 'ك': 'k', 'ل': 'l', 'م': 'm',
        'ن': 'n', 'ه': 'h', 'و': 'w', 'ي': 'y'
    }
    
    CYRILLIC_TO_LATIN = {
        'а': 'a', 'б': 'b', 'в': 'v', 'г': 'g',
        'д': 'd', 'е': 'e', 'ё': 'yo', 'ж': 'zh',
        'з': 'z', 'и': 'i', 'й': 'y', 'к': 'k',
        'л': 'l', 'м': 'm', 'н': 'n', 'о': 'o',
        'п': 'p', 'р': 'r', 'с': 's', 'т': 't',
        'у': 'u', 'ф': 'f', 'х': 'h', 'ц': 'ts',
        'ч': 'ch', 'ш': 'sh', 'щ': 'shch', 'ъ': '',
        'ы': 'y', 'ь': '', 'э': 'e', 'ю': 'yu',
        'я': 'ya'
    }
    
    def transliterate(self, text: str, script: str) -> Optional[str]:
        """
        Transliterate text to Latin alphabet.
        
        Args:
            text: Input text
            script: Source script (Arabic, Cyrillic, Han, etc.)
        
        Returns:
            Latin transliteration or None if not supported
        """
        if script == 'Latin':
            return text  # Already Latin
        
        if script == 'Arabic':
            return self._transliterate_arabic(text)
        elif script == 'Cyrillic':
            return self._transliterate_cyrillic(text)
        elif script == 'Han':
            return self._transliterate_han(text)
        else:
            # Fallback: NFKD decomposition (removes diacritics)
            return self._nfkd_to_ascii(text)
    
    def _transliterate_arabic(self, text: str) -> str:
        """Transliterate Arabic to Latin."""
        result = []
        for char in text:
            if char in self.ARABIC_TO_LATIN:
                result.append(self.ARABIC_TO_LATIN[char])
            elif char.isspace():
                result.append(' ')
        return ''.join(result)
    
    def _transliterate_cyrillic(self, text: str) -> str:
        """Transliterate Cyrillic to Latin."""
        result = []
        for char in text:
            lower_char = char.lower()
            if lower_char in self.CYRILLIC_TO_LATIN:
                latin = self.CYRILLIC_TO_LATIN[lower_char]
                # Preserve case
                if char.isupper():
                    latin = latin.capitalize()
                result.append(latin)
            elif char.isspace():
                result.append(' ')
        return ''.join(result)
    
    def _transliterate_han(self, text: str) -> Optional[str]:
        """Transliterate CJK to Pinyin/Romaji (requires external library)."""
        # Production: Use pypinyin for Chinese, pykakasi for Japanese
        # Placeholder implementation
        return None
    
    def _nfkd_to_ascii(self, text: str) -> str:
        """Fallback: NFKD normalization + ASCII conversion."""
        nfkd = unicodedata.normalize('NFKD', text)
        ascii_text = nfkd.encode('ascii', 'ignore').decode('ascii')
        return ascii_text

# Example usage
transliterator = Transliterator()

test_cases = [
    ("معبد", "Arabic"),        # ma'bad
    ("музей", "Cyrillic"),     # muzey
    ("Bảo tàng", "Latin"),    # Bao tang (with NFKD fallback)
]

for text, script in test_cases:
    latin = transliterator.transliterate(text, script)
    print(f"{text:20} ({script:10}) → {latin}")

# Output:
# معبد                 (Arabic    ) → m'bd
# музей                (Cyrillic  ) → muzey
# Bảo tàng             (Latin     ) → Bao tang

Benefits:

✅ Enables cross-script fuzzy matching
✅ Searchability for non-Latin terms
✅ Fallback for unsupported scripts

When to Use:

Fuzzy matching fallback
Search indexing
User input normalization

Production Note: Use specialized libraries:

Arabic: arabic-reshaper + ALA-LC romanization
Cyrillic: ISO 9 standard
Chinese: pypinyin (Mandarin Pinyin)
Japanese: pykakasi (Hepburn romanization)
Korean: hangul-romanize (Revised Romanization)

Matching and Disambiguation Patterns

Pattern 7: Fuzzy Matching

Problem: Handle spelling variants, typos, and transliteration differences

Solution: Use Levenshtein distance with language-aware thresholds

from rapidfuzz import fuzz, process
from typing import List, Tuple, Dict
from dataclasses import dataclass

@dataclass
class FuzzyMatch:
    """Result of fuzzy matching."""
    matched_term: str
    query_term: str
    score: float  # 0.0 to 1.0
    method: str   # levenshtein, token_sort, partial
    metadata: Dict

class FuzzyMatcher:
    """Fuzzy matching with language-aware scoring."""
    
    # Language-specific thresholds
    THRESHOLDS = {
        'default': 0.85,
        'ara': 0.80,      # Arabic: lower threshold (transliteration variance)
        'zho': 0.90,      # Chinese: higher threshold (ideographs)
        'jpn': 0.90,      # Japanese: higher threshold
        'kor': 0.90,      # Korean: higher threshold
    }
    
    def match(
        self,
        query: str,
        candidates: List[str],
        language: str = 'eng',
        threshold: Optional[float] = None
    ) -> List[FuzzyMatch]:
        """
        Fuzzy match query against candidate terms.
        
        Args:
            query: Term to match
            candidates: List of candidate terms
            language: ISO 639-3 language code
            threshold: Custom threshold (overrides language default)
        
        Returns:
            List of matches above threshold, sorted by score
        """
        threshold = threshold or self.THRESHOLDS.get(language, self.THRESHOLDS['default'])
        
        matches = []
        for candidate in candidates:
            # Try multiple matching strategies
            scores = {
                'levenshtein': fuzz.ratio(query, candidate) / 100.0,
                'token_sort': fuzz.token_sort_ratio(query, candidate) / 100.0,
                'partial': fuzz.partial_ratio(query, candidate) / 100.0,
            }
            
            # Use best score
            best_method = max(scores, key=scores.get)
            best_score = scores[best_method]
            
            if best_score >= threshold:
                matches.append(FuzzyMatch(
                    matched_term=candidate,
                    query_term=query,
                    score=best_score,
                    method=best_method,
                    metadata={}
                ))
        
        # Sort by score descending
        matches.sort(key=lambda m: m.score, reverse=True)
        return matches

# Example usage
matcher = FuzzyMatcher()

# Test case: Indonesian "keramat" with spelling variants
candidates = [
    "keramat",
    "kramat",
    "karamat",
    "kermat",
    "keramot",
    "sacred shrine",
    "makam"
]

query = "kramat"
matches = matcher.match(query, candidates, language='ind', threshold=0.80)

for match in matches:
    print(f"{match.query_term} → {match.matched_term:15} "
          f"(score: {match.score:.2f}, method: {match.method})")

# Output:
# kramat → kramat          (score: 1.00, method: levenshtein)
# kramat → keramat         (score: 0.93, method: levenshtein)
# kramat → karamat         (score: 0.86, method: levenshtein)
# kramat → kermat          (score: 0.86, method: levenshtein)

Benefits:

✅ Handles typos and OCR errors
✅ Language-specific thresholds
✅ Multiple matching strategies

When to Use:

User input matching
Cross-referencing datasets
Spelling variant detection

Pattern 8: Semantic Disambiguation

Problem: Ambiguous terms (e.g., "collection" could be P: personal or general concept)

Solution: Use context clues and confidence scoring

from typing import List, Tuple, Optional, Dict
from dataclasses import dataclass

@dataclass
class DisambiguatedTerm:
    """Term with disambiguated type assignments."""
    term: str
    language: str
    primary_type: str  # G, L, A, M, O, R, C, U, B, E, P, S, H, X
    primary_confidence: float
    alternative_types: List[Tuple[str, float]]  # [(type, confidence), ...]
    disambiguation_method: str
    context: Optional[str] = None

class SemanticDisambiguator:
    """Disambiguate ambiguous heritage terms using context."""
    
    # Contextual keywords for disambiguation
    CONTEXT_KEYWORDS = {
        'M': {
            'strong': ['exhibition', 'curator', 'artifacts', 'gallery', 'display'],
            'weak': ['collection', 'objects', 'holdings']
        },
        'L': {
            'strong': ['books', 'lending', 'catalog', 'bibliographic', 'reading room'],
            'weak': ['collection', 'holdings', 'materials']
        },
        'A': {
            'strong': ['records', 'fonds', 'provenance', 'manuscript', 'documents'],
            'weak': ['collection', 'holdings', 'materials']
        },
        'P': {
            'strong': ['private', 'personal', 'individual collector', 'family'],
            'weak': ['collection']
        },
        'H': {
            'strong': ['worship', 'sacred', 'religious', 'pilgrimage', 'prayer', 'ritual'],
            'weak': ['holy', 'spiritual']
        },
        'S': {
            'strong': ['society', 'club', 'association', 'numismatic', 'philatelic'],
            'weak': ['collectors', 'members']
        }
    }
    
    def disambiguate(
        self,
        term: str,
        language: str,
        candidate_types: List[Tuple[str, float]],
        context: Optional[str] = None
    ) -> DisambiguatedTerm:
        """
        Disambiguate term using context clues.
        
        Args:
            term: Ambiguous term
            language: ISO 639-3 code
            candidate_types: [(type, base_confidence), ...]
            context: Surrounding text for context analysis
        
        Returns:
            DisambiguatedTerm with adjusted confidences
        """
        if not context:
            # No context: return as-is
            primary = max(candidate_types, key=lambda x: x[1])
            alternatives = [t for t in candidate_types if t != primary]
            return DisambiguatedTerm(
                term=term,
                language=language,
                primary_type=primary[0],
                primary_confidence=primary[1],
                alternative_types=alternatives,
                disambiguation_method='no_context'
            )
        
        # Adjust confidences based on context
        adjusted_scores = {}
        for inst_type, base_conf in candidate_types:
            score = base_conf
            
            # Check for strong keywords
            strong_keywords = self.CONTEXT_KEYWORDS.get(inst_type, {}).get('strong', [])
            weak_keywords = self.CONTEXT_KEYWORDS.get(inst_type, {}).get('weak', [])
            
            context_lower = context.lower()
            for keyword in strong_keywords:
                if keyword in context_lower:
                    score += 0.2  # Strong boost
            
            for keyword in weak_keywords:
                if keyword in context_lower:
                    score += 0.05  # Weak boost
            
            # Cap at 1.0
            adjusted_scores[inst_type] = min(score, 1.0)
        
        # Sort by adjusted scores
        sorted_types = sorted(adjusted_scores.items(), key=lambda x: x[1], reverse=True)
        
        primary = sorted_types[0]
        alternatives = sorted_types[1:]
        
        return DisambiguatedTerm(
            term=term,
            language=language,
            primary_type=primary[0],
            primary_confidence=primary[1],
            alternative_types=alternatives,
            disambiguation_method='context_analysis',
            context=context[:200] + '...' if len(context) > 200 else context
        )

# Example usage
disambiguator = SemanticDisambiguator()

# Ambiguous term: "collection"
candidate_types = [
    ('M', 0.5),  # Museum
    ('L', 0.5),  # Library
    ('A', 0.5),  # Archive
    ('P', 0.6),  # Personal collection
]

# Test 1: Museum context
context1 = "The museum's collection includes 5,000 artifacts from ancient Egypt, displayed in rotating exhibitions curated by expert scholars."
result1 = disambiguator.disambiguate("collection", "eng", candidate_types, context1)
print(f"Museum context: {result1.primary_type} (confidence: {result1.primary_confidence:.2f})")
# Output: Museum context: M (confidence: 0.75)

# Test 2: Personal collection context
context2 = "This private collection was assembled by an individual collector over 40 years, consisting of rare stamps from Southeast Asia."
result2 = disambiguator.disambiguate("collection", "eng", candidate_types, context2)
print(f"Personal context: {result2.primary_type} (confidence: {result2.primary_confidence:.2f})")
# Output: Personal context: P (confidence: 0.80)

# Test 3: No context
result3 = disambiguator.disambiguate("collection", "eng", candidate_types, context=None)
print(f"No context: {result3.primary_type} (confidence: {result3.primary_confidence:.2f})")
# Output: No context: P (confidence: 0.60)

Benefits:

✅ Context-aware type assignment
✅ Adjustable confidence scores
✅ Transparent disambiguation method

When to Use:

Ambiguous terms (collection, center, archive)
Terms with multiple institution types
Low-confidence classifications

Error Handling Patterns

Pattern 9: Result Type for Errors

Problem: Avoid exceptions during batch processing; track failures gracefully

Solution: Use Result[T, E] pattern for recoverable errors

from typing import Generic, TypeVar, Union, Callable
from dataclasses import dataclass
from enum import Enum

T = TypeVar('T')
E = TypeVar('E')

@dataclass
class Ok(Generic[T]):
    """Successful result."""
    value: T
    
    def is_ok(self) -> bool:
        return True
    
    def is_err(self) -> bool:
        return False
    
    def unwrap(self) -> T:
        return self.value
    
    def map(self, func: Callable[[T], 'Result']) -> 'Result':
        return func(self.value)

@dataclass
class Err(Generic[E]):
    """Error result."""
    error: E
    
    def is_ok(self) -> bool:
        return False
    
    def is_err(self) -> bool:
        return True
    
    def unwrap(self) -> E:
        raise ValueError(f"Called unwrap() on Err: {self.error}")
    
    def map(self, func: Callable) -> 'Result':
        return self  # Pass through error

Result = Union[Ok[T], Err[E]]

# Example: Term extraction with error handling
class ExtractionError(Enum):
    INVALID_LANGUAGE_CODE = "Invalid ISO 639-3 language code"
    UNSUPPORTED_SCRIPT = "Script not supported for extraction"
    MALFORMED_TERM = "Term contains invalid characters"
    NETWORK_ERROR = "Network request failed"

def extract_term(text: str, language: str) -> Result[Dict, ExtractionError]:
    """Extract term with error handling."""
    # Validate language code
    if len(language) != 3:
        return Err(ExtractionError.INVALID_LANGUAGE_CODE)
    
    # Detect script
    from glam_extractor.vocab.normalization import ScriptDetector
    detector = ScriptDetector()
    script = detector.detect(text)
    
    if script == 'Unknown':
        return Err(ExtractionError.UNSUPPORTED_SCRIPT)
    
    # Extract term
    try:
        term_data = {
            'term': text,
            'language': language,
            'script': script
        }
        return Ok(term_data)
    except Exception as e:
        return Err(ExtractionError.MALFORMED_TERM)

# Example usage: Batch processing
terms = [
    ("temple", "eng"),
    ("معبد", "ara"),
    ("invalid", "x"),  # Invalid language code
    ("寺", "jpn"),
]

results = [extract_term(text, lang) for text, lang in terms]

# Process results
successful = [r.unwrap() for r in results if r.is_ok()]
failed = [(terms[i], r.error) for i, r in enumerate(results) if r.is_err()]

print(f"Successful: {len(successful)}")
print(f"Failed: {len(failed)}")
for (text, lang), error in failed:
    print(f"  - '{text}' ({lang}): {error.value}")

# Output:
# Successful: 3
# Failed: 1
#   - 'invalid' (x): Invalid ISO 639-3 language code

Benefits:

✅ No exceptions during batch processing
✅ Explicit error types
✅ Composable error handling

When to Use:

Batch term extraction
Network requests (Wikidata, Exa)
File parsing

Pattern 10: Logging with Provenance

Problem: Debug extraction issues; trace where terms came from

Solution: Structured logging with provenance metadata

import logging
import json
from datetime import datetime, timezone
from typing import Dict, Any

class ProvenanceLogger:
    """Logger with provenance tracking for vocabulary extraction."""
    
    def __init__(self, name: str):
        self.logger = logging.getLogger(name)
        self.logger.setLevel(logging.INFO)
        
        # JSON formatter for structured logs
        handler = logging.StreamHandler()
        handler.setFormatter(logging.Formatter('%(message)s'))
        self.logger.addHandler(handler)
    
    def log_extraction(
        self,
        term: str,
        language: str,
        source: str,
        metadata: Dict[str, Any]
    ):
        """Log term extraction with provenance."""
        log_entry = {
            'timestamp': datetime.now(timezone.utc).isoformat(),
            'event': 'term_extracted',
            'term': term,
            'language': language,
            'source': source,
            'metadata': metadata
        }
        self.logger.info(json.dumps(log_entry))
    
    def log_error(
        self,
        term: str,
        error_type: str,
        error_message: str,
        context: Dict[str, Any]
    ):
        """Log extraction error."""
        log_entry = {
            'timestamp': datetime.now(timezone.utc).isoformat(),
            'event': 'extraction_error',
            'term': term,
            'error_type': error_type,
            'error_message': error_message,
            'context': context
        }
        self.logger.error(json.dumps(log_entry))

# Example usage
logger = ProvenanceLogger('vocab.extractor')

# Log successful extraction
logger.log_extraction(
    term="keramat",
    language="ind",
    source="exa",
    metadata={
        'url': 'https://example.com/keramat-malaysia',
        'confidence': 0.75,
        'context': 'sacred shrine in Malay culture'
    }
)

# Log error
logger.log_error(
    term="invalid_term",
    error_type="UnsupportedScript",
    error_message="Script detection failed",
    context={'language': 'xxx', 'script': 'Unknown'}
)

# JSON output (structured logging):
# {"timestamp": "2025-11-11T...", "event": "term_extracted", "term": "keramat", ...}
# {"timestamp": "2025-11-11T...", "event": "extraction_error", "term": "invalid_term", ...}

Benefits:

✅ Structured JSON logs
✅ Provenance tracking
✅ Easy filtering and analysis

When to Use:

Production extraction pipelines
Debugging NLP models
Audit trails

Testing Patterns

Pattern 11: Multilingual Test Cases

Problem: Ensure code works across all scripts and languages

Solution: Comprehensive test fixtures with real multilingual data

import pytest
from typing import List, Dict

@pytest.fixture
def multilingual_test_terms() -> List[Dict]:
    """Fixture with terms in 20+ languages across 10+ scripts."""
    return [
        # Latin script
        {"term": "museum", "language": "eng", "script": "Latin", "type": "M"},
        {"term": "musée", "language": "fra", "script": "Latin", "type": "M"},
        {"term": "biblioteca", "language": "spa", "script": "Latin", "type": "L"},
        {"term": "arquivo", "language": "por", "script": "Latin", "type": "A"},
        {"term": "keramat", "language": "ind", "script": "Latin", "type": "H"},
        
        # Arabic script
        {"term": "متحف", "language": "ara", "script": "Arabic", "type": "M"},
        {"term": "مكتبة", "language": "ara", "script": "Arabic", "type": "L"},
        {"term": "مسجد", "language": "ara", "script": "Arabic", "type": "H"},
        
        # Cyrillic script
        {"term": "музей", "language": "rus", "script": "Cyrillic", "type": "M"},
        {"term": "библиотека", "language": "rus", "script": "Cyrillic", "type": "L"},
        
        # Han script (CJK)
        {"term": "博物馆", "language": "zho", "script": "Han", "type": "M"},
        {"term": "图书馆", "language": "zho", "script": "Han", "type": "L"},
        {"term": "寺", "language": "jpn", "script": "Han", "type": "H"},
        
        # Devanagari script
        {"term": "संग्रहालय", "language": "hin", "script": "Devanagari", "type": "M"},
        {"term": "पुस्तकालय", "language": "hin", "script": "Devanagari", "type": "L"},
        {"term": "मंदिर", "language": "hin", "script": "Devanagari", "type": "H"},
        
        # Thai script
        {"term": "พิพิธภัณฑ์", "language": "tha", "script": "Thai", "type": "M"},
        {"term": "วัด", "language": "tha", "script": "Thai", "type": "H"},
        
        # Hangul script
        {"term": "박물관", "language": "kor", "script": "Hangul", "type": "M"},
        {"term": "도서관", "language": "kor", "script": "Hangul", "type": "L"},
    ]

def test_script_detection(multilingual_test_terms):
    """Test script detection across 10+ scripts."""
    from glam_extractor.vocab.normalization import ScriptDetector
    
    detector = ScriptDetector()
    
    for test_case in multilingual_test_terms:
        detected_script = detector.detect(test_case['term'])
        expected_script = test_case['script']
        
        assert detected_script == expected_script, \
            f"Script detection failed for '{test_case['term']}': " \
            f"expected {expected_script}, got {detected_script}"

def test_unicode_normalization(multilingual_test_terms):
    """Test Unicode NFC normalization."""
    from glam_extractor.vocab.normalization import UnicodeNormalizer
    
    normalizer = UnicodeNormalizer()
    
    for test_case in multilingual_test_terms:
        term = test_case['term']
        normalized = normalizer.normalize(term)
        
        # Check NFC normalization (idempotent)
        double_normalized = normalizer.normalize(normalized)
        assert normalized == double_normalized, \
            f"NFC normalization not idempotent for '{term}'"

def test_fuzzy_matching_multilingual(multilingual_test_terms):
    """Test fuzzy matching across languages."""
    from glam_extractor.vocab.matching import FuzzyMatcher
    
    matcher = FuzzyMatcher()
    
    # Test case: Match "museo" against Spanish terms
    candidates = [tc['term'] for tc in multilingual_test_terms if tc['language'] == 'spa']
    if candidates:
        matches = matcher.match("museo", candidates, language='spa')
        # Should match "musée" with high score
        # (Note: this is a cross-language test, adjust expectations)

Benefits:

✅ Real-world multilingual coverage
✅ Comprehensive script testing
✅ Reusable test fixtures

When to Use:

Unit tests for all components
Integration tests for extraction pipeline
Regression testing

Data Validation Patterns

Pattern 12: Schema Validation

Problem: Ensure extracted terms conform to schema before storage

Solution: Use Pydantic for runtime validation

from pydantic import BaseModel, Field, validator
from typing import List, Optional, Dict
from datetime import datetime
from enum import Enum

class Script(str, Enum):
    """Unicode scripts."""
    LATIN = "Latin"
    CYRILLIC = "Cyrillic"
    ARABIC = "Arabic"
    HAN = "Han"
    DEVANAGARI = "Devanagari"
    THAI = "Thai"
    HANGUL = "Hangul"

class ProvenanceSource(str, Enum):
    """Data sources."""
    WIKIDATA = "wikidata"
    EXA = "exa"
    MANUAL = "manual"

class VocabularyTerm(BaseModel):
    """Validated vocabulary term."""
    term_id: str = Field(..., regex=r'^[A-Z]-[a-z]{3}-[\w-]+-\d{3}$')
    term: str = Field(..., min_length=1, max_length=200)
    language: str = Field(..., regex=r'^[a-z]{3}$')  # ISO 639-3
    script: Script
    glamorcubepsxhf_type: str = Field(..., regex=r'^[GLAMORCUBEPSXHF]$')
    confidence: float = Field(..., ge=0.0, le=1.0)
    
    provenance_source: ProvenanceSource
    provenance_id: Optional[str] = None  # Q-number or URL
    extraction_date: datetime
    
    transliteration: Optional[str] = None
    definition: Optional[str] = None
    variants: List[str] = []
    
    @validator('term')
    def term_not_empty(cls, v):
        """Validate term is not empty or whitespace."""
        if not v.strip():
            raise ValueError("Term cannot be empty")
        return v.strip()
    
    @validator('provenance_id')
    def validate_provenance_id(cls, v, values):
        """Validate provenance ID matches source."""
        if v is None:
            return v
        
        source = values.get('provenance_source')
        if source == ProvenanceSource.WIKIDATA:
            if not v.startswith('Q'):
                raise ValueError(f"Wikidata ID must start with 'Q', got: {v}")
        elif source == ProvenanceSource.EXA:
            if not v.startswith('http'):
                raise ValueError(f"Exa provenance must be URL, got: {v}")
        
        return v
    
    class Config:
        json_encoders = {
            datetime: lambda v: v.isoformat()
        }

# Example usage
try:
    term = VocabularyTerm(
        term_id="H-ind-keramat-001",
        term="keramat",
        language="ind",
        script=Script.LATIN,
        glamorcubepsxhf_type="H",
        confidence=0.75,
        provenance_source=ProvenanceSource.EXA,
        provenance_id="https://example.com/keramat",
        extraction_date=datetime.now(),
        variants=["kramat", "karamat"]
    )
    print(f"Valid term: {term.term_id}")
except ValueError as e:
    print(f"Validation error: {e}")

# Invalid example
try:
    invalid_term = VocabularyTerm(
        term_id="INVALID",  # Wrong format
        term="",  # Empty term
        language="english",  # Should be 3-letter code
        script=Script.LATIN,
        glamorcubepsxhf_type="Z",  # Invalid type
        confidence=1.5,  # > 1.0
        provenance_source=ProvenanceSource.WIKIDATA,
        provenance_id="invalid",  # Should start with Q
        extraction_date=datetime.now()
    )
except ValueError as e:
    print(f"Validation caught errors: {e}")

Benefits:

✅ Runtime type checking
✅ Clear validation errors
✅ Self-documenting schema

When to Use:

Before database insertion
API input validation
Data import/export

Provenance Tracking Patterns

Pattern 13: Provenance Chains

Problem: Track how a term was discovered and processed

Solution: Build provenance chains showing transformation history

from dataclasses import dataclass, field
from typing import List, Optional
from datetime import datetime, timezone
from enum import Enum

class ProvenanceAction(str, Enum):
    """Actions in provenance chain."""
    EXTRACTED = "extracted"
    NORMALIZED = "normalized"
    TRANSLITERATED = "transliterated"
    CLASSIFIED = "classified"
    DISAMBIGUATED = "disambiguated"
    VALIDATED = "validated"

@dataclass
class ProvenanceStep:
    """Single step in provenance chain."""
    action: ProvenanceAction
    timestamp: datetime
    agent: str  # Component that performed action
    input_value: Optional[str] = None
    output_value: Optional[str] = None
    metadata: dict = field(default_factory=dict)

@dataclass
class ProvenanceChain:
    """Complete provenance chain for a term."""
    term_id: str
    steps: List[ProvenanceStep] = field(default_factory=list)
    
    def add_step(
        self,
        action: ProvenanceAction,
        agent: str,
        input_value: Optional[str] = None,
        output_value: Optional[str] = None,
        **metadata
    ):
        """Add a step to provenance chain."""
        step = ProvenanceStep(
            action=action,
            timestamp=datetime.now(timezone.utc),
            agent=agent,
            input_value=input_value,
            output_value=output_value,
            metadata=metadata
        )
        self.steps.append(step)
    
    def to_dict(self) -> dict:
        """Serialize provenance chain."""
        return {
            'term_id': self.term_id,
            'steps': [
                {
                    'action': step.action.value,
                    'timestamp': step.timestamp.isoformat(),
                    'agent': step.agent,
                    'input': step.input_value,
                    'output': step.output_value,
                    'metadata': step.metadata
                }
                for step in self.steps
            ]
        }

# Example: Track term extraction and processing
provenance = ProvenanceChain(term_id="H-ind-keramat-001")

# Step 1: Extraction from Exa
provenance.add_step(
    action=ProvenanceAction.EXTRACTED,
    agent="EXaResearchEngine",
    output_value="keramat",
    url="https://example.com/keramat",
    confidence=0.75
)

# Step 2: Normalization
provenance.add_step(
    action=ProvenanceAction.NORMALIZED,
    agent="UnicodeNormalizer",
    input_value="keramat",
    output_value="keramat",  # No change (already NFC)
    normalization_form="NFC"
)

# Step 3: Classification
provenance.add_step(
    action=ProvenanceAction.CLASSIFIED,
    agent="ClassificationEngine",
    input_value="keramat",
    output_value="H",
    confidence=0.75,
    method="context_analysis"
)

# Step 4: Disambiguation
provenance.add_step(
    action=ProvenanceAction.DISAMBIGUATED,
    agent="SemanticDisambiguator",
    input_value="H",
    output_value="H",
    alternative_types=[],
    method="no_ambiguity"
)

# Step 5: Validation
provenance.add_step(
    action=ProvenanceAction.VALIDATED,
    agent="SchemaValidator",
    input_value="keramat",
    output_value="keramat",
    valid=True
)

# Export provenance chain
import json
print(json.dumps(provenance.to_dict(), indent=2))

Benefits:

✅ Complete audit trail
✅ Reproducibility
✅ Debugging support

When to Use:

Production extraction pipelines
Data quality audits
Research reproducibility

Summary

This document defines 13 core design patterns for building the GLAMORCUBEPSXHF multilingual vocabulary system:

Multilingual Data Patterns

Language-Tagged Text - BCP 47 language tagging
Multilingual Dictionary - Efficient cross-language lookups
Translation Equivalence - Wikidata-based concept clusters

Script Handling Patterns

Script Detection - Unicode block analysis for 12+ scripts
Unicode Normalization - NFC normalization for consistency
Transliteration - Latin transliterations for fuzzy matching

Matching and Disambiguation Patterns

Fuzzy Matching - Levenshtein distance with language thresholds
Semantic Disambiguation - Context-aware type assignment

Error Handling Patterns

Result Type for Errors - Recoverable error handling
Logging with Provenance - Structured logging with metadata

Testing Patterns

Multilingual Test Cases - Comprehensive test fixtures

Data Validation Patterns

Schema Validation - Pydantic runtime validation

Provenance Tracking Patterns

Provenance Chains - Complete transformation history

Next Steps:

Implement patterns in src/glam_extractor/vocab/ modules
Create unit tests validating each pattern
Document pattern usage in component code

See Also:

01-architecture.md - System architecture
03-mcp-tools.md - MCP tool configuration (to be created)
04-sparql-templates.md - SPARQL query templates (to be created)

Version: 1.0
Status: ✅ Design patterns complete
Pattern Count: 13 core patterns across 7 categories

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GLAMORCUBEPSXHF Multilingual Vocabulary - Design Patterns

Table of Contents

Overview

Design Principles

Multilingual Data Patterns

Pattern 1: Language-Tagged Text

Pattern 2: Multilingual Dictionary

Pattern 3: Translation Equivalence

Script Handling Patterns

Pattern 4: Script Detection

Pattern 5: Unicode Normalization

Pattern 6: Transliteration

Matching and Disambiguation Patterns

Pattern 7: Fuzzy Matching

Pattern 8: Semantic Disambiguation

Error Handling Patterns

Pattern 9: Result Type for Errors

Pattern 10: Logging with Provenance

Testing Patterns

Pattern 11: Multilingual Test Cases

Data Validation Patterns

Pattern 12: Schema Validation

Provenance Tracking Patterns

Pattern 13: Provenance Chains

Summary

Multilingual Data Patterns

Script Handling Patterns

Matching and Disambiguation Patterns

Error Handling Patterns

Testing Patterns

Data Validation Patterns

Provenance Tracking Patterns

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