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Full Quranic Corpus Parser — builds the complete root graph from real data.
Data sources:
1. roots_lane.json — 1,651 roots with Lane's Lexicon definitions,
frequencies, and morphological forms
2. quran-morphology.txt — Quranic Arabic Corpus v0.4, per-word
morphological annotation for all 77,430 words
Output:
- Complete NetworkX graph with:
- ALL root nodes (with frequency, semantic field, confidence)
- ALL lemma/form nodes (with pattern, POS, frequency)
- Derivation edges (root → lemma)
- Semantic co-occurrence edges (root ↔ root, weighted by verse co-occurrence)
"""
import json
import re
from collections import defaultdict, Counter
from pathlib import Path
import networkx as nx
CORPUS_DIR = Path(__file__).parent / "corpus"
def parse_roots_lane():
"""Parse roots_lane.json — all 1,651 roots with morphological forms."""
path = CORPUS_DIR / "roots_lane.json"
with open(path, "r", encoding="utf-8") as f:
data = json.load(f)
roots = {}
for entry in data["roots"]:
root_ar = entry["root"]
roots[root_ar] = {
"buckwalter": entry.get("root_buckwalter", ""),
"frequency": entry.get("quran_frequency", 0),
"summary_en": entry.get("summary_en", ""),
"confidence": entry.get("confidence", ""),
"source": entry.get("source", ""),
"forms": [],
}
for form in entry.get("morphological_forms", []):
roots[root_ar]["forms"].append({
"pattern": form.get("form_pattern", ""),
"arabic": form.get("form_arabic", ""),
"name": form.get("form_name", ""),
"category": form.get("form_category", ""),
"example": form.get("example_word", ""),
"occurrences": form.get("occurrences", 0),
})
return roots, data.get("metadata", {})
def parse_morphology_corpus():
"""
Parse quran-morphology.txt — every word in the Quran with root + lemma.
Returns:
word_data: list of (chapter, verse, word_pos, arabic, pos, root, lemma, features_dict)
verse_roots: dict mapping (chapter, verse) → set of roots in that verse
root_lemmas: dict mapping root → Counter of lemma occurrences
root_forms: dict mapping root → Counter of verb form (VF:N) occurrences
"""
path = CORPUS_DIR / "quran-morphology.txt"
word_data = []
verse_roots = defaultdict(set)
root_lemmas = defaultdict(Counter)
root_forms = defaultdict(Counter)
root_pos_counts = defaultdict(Counter)
with open(path, "r", encoding="utf-8") as f:
for line in f:
line = line.strip()
if not line or line.startswith("#"):
continue
parts = line.split("\t")
if len(parts) < 4:
continue
position = parts[0]
arabic = parts[1]
pos = parts[2]
features_str = parts[3]
pos_parts = position.split(":")
if len(pos_parts) < 3:
continue
chapter = int(pos_parts[0])
verse = int(pos_parts[1])
word_pos = int(pos_parts[2])
features = {}
for feat in features_str.split("|"):
if ":" in feat:
key, val = feat.split(":", 1)
features[key] = val
else:
features[feat] = True
root = features.get("ROOT", None)
lemma = features.get("LEM", None)
verb_form = features.get("VF", None)
if root:
verse_roots[(chapter, verse)].add(root)
if lemma:
root_lemmas[root][lemma] += 1
if verb_form:
root_forms[root][verb_form] += 1
root_pos_counts[root][pos] += 1
word_data.append((chapter, verse, word_pos, arabic, pos, root, lemma, features))
return word_data, dict(verse_roots), dict(root_lemmas), dict(root_forms), dict(root_pos_counts)
def build_cooccurrence_edges(verse_roots, min_cooccurrence=3):
"""
Build semantic co-occurrence edges from verse-level root co-occurrence.
Two roots that appear in the same verse are semantically related.
Weight = number of verses they co-occur in.
"""
cooccurrence = Counter()
for (chapter, verse), roots in verse_roots.items():
roots_list = sorted(roots)
for i in range(len(roots_list)):
for j in range(i + 1, len(roots_list)):
pair = (roots_list[i], roots_list[j])
cooccurrence[pair] += 1
edges = []
for (root_a, root_b), count in cooccurrence.items():
if count >= min_cooccurrence:
edges.append((root_a, root_b, count))
edges.sort(key=lambda x: -x[2])
return edges
def build_full_graph(min_cooccurrence=3):
"""Build the complete Quranic root graph from all data sources."""
print("Parsing roots_lane.json...")
roots_data, metadata = parse_roots_lane()
print(f" {len(roots_data)} roots loaded")
print("Parsing quran-morphology.txt...")
word_data, verse_roots, root_lemmas, root_forms, root_pos = parse_morphology_corpus()
print(f" {len(word_data)} word tokens parsed")
print(f" {len(verse_roots)} verses processed")
print(f" {len(root_lemmas)} unique roots found in morphology corpus")
print("Building co-occurrence edges...")
cooc_edges = build_cooccurrence_edges(verse_roots, min_cooccurrence)
print(f" {len(cooc_edges)} co-occurrence edges (min {min_cooccurrence} shared verses)")
G = nx.DiGraph()
all_roots_in_corpus = set(root_lemmas.keys())
all_roots_in_lane = set(roots_data.keys())
corpus_root_freq = Counter()
for (ch, v), roots in verse_roots.items():
for r in roots:
corpus_root_freq[r] += 1
for root_ar, data in roots_data.items():
G.add_node(root_ar, type="root", depth=0,
frequency=data["frequency"],
summary=data["summary_en"],
buckwalter=data["buckwalter"],
confidence=data["confidence"],
num_forms=len(data["forms"]),
num_lemmas=len(root_lemmas.get(root_ar, {})))
for root_ar in all_roots_in_corpus - all_roots_in_lane:
freq = corpus_root_freq.get(root_ar, 0)
if freq >= 2:
G.add_node(root_ar, type="root", depth=0,
frequency=freq, summary="", buckwalter="",
confidence="corpus_only", num_forms=0,
num_lemmas=len(root_lemmas.get(root_ar, {})))
for root_ar, data in roots_data.items():
for form in data["forms"]:
form_name = form["example"] or form["arabic"]
if not form_name:
continue
node_id = f"{root_ar}:{form_name}"
depth = 1 if form["pattern"].startswith("form_") else 2
G.add_node(node_id, type="derived", depth=depth,
arabic=form_name, pattern=form["pattern"],
pattern_name=form["name"],
category=form["category"],
frequency=form["occurrences"],
source_root=root_ar)
G.add_edge(root_ar, node_id, relation="derivation",
pattern=form["pattern"])
for root_ar, lemmas in root_lemmas.items():
if not G.has_node(root_ar):
continue
for lemma, count in lemmas.most_common():
node_id = f"{root_ar}:{lemma}"
if G.has_node(node_id):
continue
G.add_node(node_id, type="derived", depth=2,
arabic=lemma, pattern="corpus",
frequency=count, source_root=root_ar)
G.add_edge(root_ar, node_id, relation="derivation",
pattern="corpus", weight=count)
for root_a, root_b, count in cooc_edges:
if G.has_node(root_a) and G.has_node(root_b):
G.add_edge(root_a, root_b, relation="semantic",
co_occurrence=count)
G.add_edge(root_b, root_a, relation="semantic",
co_occurrence=count)
return G, metadata
def print_stats(G):
"""Print comprehensive graph statistics."""
roots = [n for n, d in G.nodes(data=True) if d.get("type") == "root"]
derived = [n for n, d in G.nodes(data=True) if d.get("type") == "derived"]
deriv_edges = [(u, v) for u, v, d in G.edges(data=True) if d.get("relation") == "derivation"]
sem_edges = [(u, v) for u, v, d in G.edges(data=True) if d.get("relation") == "semantic"]
root_freqs = sorted([G.nodes[n].get("frequency", 0) for n in roots], reverse=True)
print(f"\n{'='*60}")
print(f"QURANIC ROOT GRAPH — COMPLETE")
print(f"{'='*60}")
print(f" Root nodes: {len(roots)}")
print(f" Derived nodes: {len(derived)}")
print(f" Total nodes: {G.number_of_nodes()}")
print(f" Derivation edges: {len(deriv_edges)}")
print(f" Semantic edges: {len(sem_edges)} (co-occurrence)")
print(f" Total edges: {G.number_of_edges()}")
print(f"\n Top 20 roots by Quranic frequency:")
top_roots = sorted(roots, key=lambda n: G.nodes[n].get("frequency", 0), reverse=True)[:20]
for i, r in enumerate(top_roots, 1):
d = G.nodes[r]
summ = d.get("summary", "")[:60]
print(f" {i:2d}. {r:8s} freq={d['frequency']:5d} forms={d.get('num_forms',0):2d} {summ}")
print(f"\n Frequency distribution:")
print(f" Roots with freq > 100: {sum(1 for f in root_freqs if f > 100)}")
print(f" Roots with freq 10-100: {sum(1 for f in root_freqs if 10 <= f <= 100)}")
print(f" Roots with freq 1-9: {sum(1 for f in root_freqs if 1 <= f < 10)}")
top_cooc = sorted(
[(u, v, d["co_occurrence"]) for u, v, d in G.edges(data=True)
if d.get("relation") == "semantic" and u < v],
key=lambda x: -x[2]
)[:15]
print(f"\n Top 15 co-occurring root pairs:")
for r1, r2, count in top_cooc:
s1 = G.nodes[r1].get("summary", "")[:25]
s2 = G.nodes[r2].get("summary", "")[:25]
print(f" {r1:8s} <-> {r2:8s} co_occ={count:4d} ({s1} / {s2})")
def save_graph(G, path):
"""Save graph as JSON for reloading."""
nodes = {}
for n, d in G.nodes(data=True):
nodes[n] = dict(d)
edges = []
for u, v, d in G.edges(data=True):
edges.append({"source": u, "target": v, **d})
with open(path, "w", encoding="utf-8") as f:
json.dump({"nodes": nodes, "edges": edges}, f, ensure_ascii=False, indent=1)
print(f"\n Graph saved to {path}")
if __name__ == "__main__":
import sys
min_co = int(sys.argv[1]) if len(sys.argv) > 1 else 3
G, meta = build_full_graph(min_cooccurrence=min_co)
print_stats(G)
save_graph(G, "E:/ABU/NEOCORTEX/data/full_graph.json")
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