Advanced Analytical Queries

This document provides complex query examples for sophisticated analysis using the Quran Knowledge Graph.

1. Multi-dimensional Thematic Analysis

Description

Performs a multi-dimensional analysis of how different themes intersect across the Quran, revealing complex thematic relationships.

Query

// Find verses that address multiple themes
MATCH (v:Verse)
MATCH (v)-[:ADDRESSES_TOPIC]->(t:Topic)
WHERE t.name IN $theme_list
WITH v, collect(DISTINCT t.name) AS themes
WHERE size(themes) >= $min_themes

// Get chapter information
MATCH (c:Chapter)-[:CONTAINS]->(v)

// Return results with theme combinations
RETURN v.verse_key, v.text_uthmani,
       c.name_english AS chapter_name,
       c.revelation_place AS revelation_place,
       themes
ORDER BY size(themes) DESC, v.verse_key
LIMIT $limit

Parameters

theme_list: List of themes to analyze (e.g., [“Mercy”, “Justice”, “Faith”, “Accountability”])
min_themes: Minimum number of themes that must be present (e.g., 2)
limit: Maximum number of verses to return (e.g., 30)

Expected Results

A table showing verses that address multiple themes:

verse_key	text_uthmani	chapter_name	revelation_place	themes
4:136	يَا أَيُّهَا الَّذِينَ آمَنُوا آمِنُوا بِاللَّهِ وَرَسُولِهِ…	The Women	Medinan	[“Faith”, “Accountability”, “Justice”]
2:177	لَّيْسَ الْبِرَّ أَن تُوَلُّوا وُجُوهَكُمْ قِبَلَ الْمَشْرِقِ وَالْمَغْرِبِ…	The Cow	Medinan	[“Faith”, “Mercy”, “Justice”]
…	…	…	…	…

Variations

Analyze theme co-occurrence: WITH themes, count(v) AS verse_count RETURN themes, verse_count ORDER BY verse_count DESC
Compare across revelation periods: WITH c.revelation_place AS place, themes, count(v) AS verse_count RETURN place, themes, verse_count ORDER BY place, verse_count DESC
Create theme correlation matrix: UNWIND themes AS theme1 UNWIND themes AS theme2 WHERE theme1 < theme2 WITH theme1, theme2, count(v) AS co_occurrences RETURN theme1, theme2, co_occurrences ORDER BY co_occurrences DESC

2. Chronological Theme Evolution Analysis

Description

Analyzes how themes evolve throughout the chronological order of revelation, revealing the development of concepts over time.

Query

// Match verses addressing the topic
MATCH (t:Topic {name: $topic_name})<-[:ADDRESSES_TOPIC]-(v:Verse)<-[:CONTAINS]-(c:Chapter)
WHERE c.revelation_order IS NOT NULL

// Group by revelation period
WITH c.revelation_order AS rev_order,
     CASE
       WHEN c.revelation_order <= 30 THEN "Early Meccan"
       WHEN c.revelation_order <= 85 THEN "Late Meccan"
       ELSE "Medinan"
     END AS period,
     c.name_english AS chapter,
     count(v) AS verse_count,
     collect(v.verse_key) AS verses

// Calculate cumulative metrics
WITH period, rev_order, chapter, verse_count, verses
ORDER BY rev_order
WITH period, collect({rev_order: rev_order, chapter: chapter, verse_count: verse_count, verses: verses}) AS chapters
UNWIND chapters AS chapter_data

// Return detailed evolution data
RETURN period,
       chapter_data.rev_order AS revelation_order,
       chapter_data.chapter AS chapter_name,
       chapter_data.verse_count AS verse_count,
       chapter_data.verses[0..3] AS sample_verses
ORDER BY revelation_order

Parameters

topic_name: Name of the topic to analyze (e.g., “Faith”)

Expected Results

A table showing the evolution of the topic throughout revelation:

period	revelation_order	chapter_name	verse_count	sample_verses
Early Meccan	1	The Clot	2	[“96:1”, “96:2”]
Early Meccan	5	The Wrapped One	3	[“73:2”, “73:8”, “73:9”]
…	…	…	…	…
Late Meccan	39	The Groups	8	[“39:2”, “39:11”, “39:14”]
…	…	…	…	…
Medinan	87	The Cow	15	[“2:3”, “2:4”, “2:8”]
…	…	…	…	…

Variations

Calculate density metrics: WITH period, sum(chapter_data.verse_count) AS period_verses, count(chapter_data) AS period_chapters RETURN period, period_verses, period_chapters, period_verses * 1.0 / period_chapters AS verses_per_chapter
Compare multiple topics: MATCH (t:Topic)<-[:ADDRESSES_TOPIC]-(v:Verse)<-[:CONTAINS]-(c:Chapter) WHERE t.name IN $topic_list AND c.revelation_order IS NOT NULL WITH t.name AS topic, c.revelation_order AS rev_order, count(v) AS verse_count ORDER BY rev_order, topic RETURN rev_order, collect({topic: topic, count: verse_count}) AS topic_counts
Analyze linguistic changes: MATCH (t:Topic {name: $topic_name})<-[:ADDRESSES_TOPIC]-(v:Verse)-[:CONTAINS_WORD]->(w:Word)-[:HAS_ROOT]->(r:Root) WITH period, r.text_clean AS root, count(w) AS occurrences WHERE occurrences > 3 RETURN period, collect({root: root, count: occurrences}) AS root_usage ORDER BY period

3. Structural Pattern Analysis

Description

Analyzes structural patterns in the Quran, such as verse length distribution, to identify potential literary or rhetorical patterns.

Query

// Get verse word counts
MATCH (c:Chapter)-[:CONTAINS]->(v:Verse)-[:CONTAINS_WORD]->(w:Word)
WITH c, v, count(w) AS word_count

// Calculate chapter statistics
WITH c, avg(word_count) AS avg_length,
     min(word_count) AS min_length,
     max(word_count) AS max_length,
     stDev(word_count) AS std_dev,
     percentileCont(word_count, 0.5) AS median_length,
     count(v) AS verse_count

// Return chapter statistics
RETURN c.chapter_number, c.name_english, c.revelation_place,
       avg_length, median_length, min_length, max_length, std_dev,
       verse_count
ORDER BY c.chapter_number

Parameters

None required

Expected Results

A table showing structural patterns across chapters:

chapter_number	name_english	revelation_place	avg_length	median_length	min_length	max_length	std_dev	verse_count
1	The Opening	Meccan	4.0	4.0	3	5	0.82	7
2	The Cow	Medinan	16.2	14.0	3	128	12.4	286
…	…	…	…	…	…	…	…	…

Variations

Analyze verse position patterns: WITH c, v.verse_number AS position, word_count ORDER BY c.chapter_number, position WITH c.chapter_number AS chapter, collect({position: position, length: word_count}) AS verse_lengths RETURN chapter, verse_lengths
Find structural anomalies: WITH c, v, word_count, avg(word_count) OVER (PARTITION BY c.id) AS chapter_avg WHERE abs(word_count - chapter_avg) > 2 * stDev(word_count) OVER (PARTITION BY c.id) RETURN c.name_english, v.verse_key, word_count, chapter_avg, word_count - chapter_avg AS deviation
Compare Meccan vs Medinan structure: WITH c.revelation_place AS place, avg(word_count) AS avg_length, stDev(word_count) AS std_dev RETURN place, avg_length, std_dev

4. Semantic Coherence Analysis

Description

Analyzes the semantic coherence of chapters by measuring the similarity between adjacent verses and identifying thematic transitions.

Query

// Get verses in a specific chapter
MATCH (c:Chapter {chapter_number: $chapter_number})-[:CONTAINS]->(v:Verse)
WITH c, v
ORDER BY v.verse_number

// Collect verses with their embeddings
WITH c, collect({key: v.verse_key, number: v.verse_number, embedding: v.embedding}) AS verses

// Calculate similarity between adjacent verses
UNWIND range(0, size(verses)-2) AS i
WITH c, verses, i,
     verses[i].key AS verse1,
     verses[i].number AS number1,
     verses[i+1].key AS verse2,
     verses[i+1].number AS number2,
     vector_similarity(verses[i].embedding, verses[i+1].embedding) AS similarity

// Identify significant transitions (low similarity points)
WITH c, verse1, number1, verse2, number2, similarity,
     avg(similarity) OVER () AS avg_similarity,
     stDev(similarity) OVER () AS std_dev
WHERE similarity < avg_similarity - $transition_threshold * std_dev

// Return potential thematic transitions
RETURN verse1, verse2, similarity,
       avg_similarity, std_dev,
       (avg_similarity - similarity) / std_dev AS transition_strength
ORDER BY transition_strength DESC
LIMIT $limit

Parameters

chapter_number: Chapter number to analyze (e.g., 2)
transition_threshold: Number of standard deviations below average to identify transitions (e.g., 1.5)
limit: Maximum number of transitions to return (e.g., 10)

Expected Results

A table showing potential thematic transitions within the chapter:

verse1	verse2	similarity	avg_similarity	std_dev	transition_strength
2:141	2:142	0.32	0.78	0.15	3.07
2:176	2:177	0.41	0.78	0.15	2.47
…	…	…	…	…	…

Variations

Identify coherent sections: WHERE similarity > avg_similarity + $coherence_threshold * std_dev WITH verse1, verse2, similarity ORDER BY verse1 WITH collect({start: verse1, end: verse2, similarity: similarity}) AS coherent_pairs
Compare with topic transitions: MATCH (v1:Verse {verse_key: verse1})-[:ADDRESSES_TOPIC]->(t1:Topic) WITH verse1, verse2, similarity, transition_strength, collect(DISTINCT t1.name) AS topics1 MATCH (v2:Verse {verse_key: verse2})-[:ADDRESSES_TOPIC]->(t2:Topic) WITH verse1, verse2, similarity, transition_strength, topics1, collect(DISTINCT t2.name) AS topics2 RETURN verse1, verse2, similarity, transition_strength, topics1, topics2, size([x IN topics1 WHERE NOT x IN topics2]) AS topic_changes
Analyze global coherence: WITH c.chapter_number AS chapter, avg(similarity) AS coherence, min(similarity) AS min_coherence, max(similarity) AS max_coherence RETURN chapter, coherence, min_coherence, max_coherence ORDER BY coherence DESC

5. Network Analysis of Thematic Relationships

Description

Performs network analysis on the thematic structure of the Quran, identifying central themes and their relationships.

Query

// Calculate how many verses address each topic
MATCH (t:Topic)<-[:ADDRESSES_TOPIC]-(v:Verse)
WITH t, count(v) AS verse_count

// Find relationships between topics based on co-occurrence in verses
MATCH (t)-[:ADDRESSES_TOPIC]-(v:Verse)-[:ADDRESSES_TOPIC]->(t2:Topic)
WHERE t <> t2
WITH t, t2, count(v) AS shared_verses, verse_count

// Calculate relationship strength
WITH t.name AS topic1, t2.name AS topic2,
     shared_verses, verse_count,
     shared_verses * 1.0 / verse_count AS relationship_strength
WHERE relationship_strength > $strength_threshold

// Calculate network metrics
WITH topic1, collect({topic: topic2, strength: relationship_strength}) AS connections,
     count(*) AS degree
ORDER BY degree DESC

// Return network analysis results
RETURN topic1 AS topic,
       degree AS connection_count,
       connections[0..5] AS strongest_connections
LIMIT $limit

Parameters

strength_threshold: Minimum relationship strength to include (e.g., 0.2)
limit: Maximum number of topics to return (e.g., 20)

Expected Results

A table showing network analysis of thematic relationships:

topic	connection_count	strongest_connections
Faith	18	`[{topic: "Belief in Allah", strength: 0.85}, {topic: "Guidance", strength: 0.72}, ...]`
Justice	15	`[{topic: "Rights", strength: 0.78}, {topic: "Accountability", strength: 0.65}, ...]`
…	…	…

Variations

Calculate centrality metrics: WITH topic1, degree, connections, size(connections) * 1.0 / $total_topics AS centrality RETURN topic1, degree, centrality, connections
Identify thematic communities: This would require implementing community detection algorithms in application code using the relationship data
Analyze second-degree connections: WITH topic1, connections UNWIND connections AS conn WITH topic1, conn.topic AS intermediate MATCH (t:Topic {name: intermediate})<-[:ADDRESSES_TOPIC]-(v:Verse)-[:ADDRESSES_TOPIC]->(t2:Topic) WHERE t2.name <> topic1 AND t2.name <> intermediate WITH topic1, intermediate, t2.name AS topic2, count(v) AS indirect_strength RETURN topic1, intermediate, topic2, indirect_strength ORDER BY indirect_strength DESC LIMIT 20

6. Comparative Analysis of Divine Attributes

Description

Analyzes how divine attributes are presented across different parts of the Quran, revealing patterns in the description of God.

Query

// Find verses addressing divine attributes
MATCH (t:Topic {name: "Divine Attributes"})<-[:ADDRESSES_TOPIC]-(v:Verse)<-[:CONTAINS]-(c:Chapter)

// Extract specific attributes using word roots
MATCH (v)-[:CONTAINS_WORD]->(w:Word)-[:HAS_ROOT]->(r:Root)
WHERE r.text_clean IN $attribute_roots

// Group by revelation period and attribute
WITH c.revelation_place AS place,
     r.text_clean AS attribute_root,
     r.english_trilateral AS attribute_name,
     count(DISTINCT v) AS verse_count,
     collect(DISTINCT v.verse_key)[0..3] AS sample_verses

// Return comparative analysis
RETURN place, attribute_root, attribute_name, verse_count, sample_verses
ORDER BY place, verse_count DESC

Parameters

attribute_roots: List of root words representing divine attributes (e.g., [“رحم”, “علم”, “قدر”, “حكم”])

Expected Results

A table comparing divine attributes across revelation periods:

place	attribute_root	attribute_name	verse_count	sample_verses
Meccan	رحم	rhm (mercy)	45	[“7:156”, “6:12”, “6:54”]
Meccan	علم	’lm (knowledge)	38	[“6:59”, “10:61”, “20:98”]
…	…	…	…	…
Medinan	حكم	hkm (wisdom)	28	[“2:129”, “4:26”, “5:38”]
…	…	…	…	…

Variations

Calculate relative frequency: WITH place, attribute_root, attribute_name, verse_count, sample_verses, sum(verse_count) OVER (PARTITION BY place) AS total_attributes RETURN place, attribute_root, attribute_name, verse_count, verse_count * 100.0 / total_attributes AS percentage, sample_verses
Analyze contextual associations: MATCH (v)-[:ADDRESSES_TOPIC]->(other:Topic) WHERE other.name <> "Divine Attributes" WITH attribute_root, other.name AS associated_topic, count(v) AS co_occurrences RETURN attribute_root, collect({topic: associated_topic, count: co_occurrences})[0..5] AS context
Compare with explicit names: MATCH (v)-[:CONTAINS_WORD]->(w:Word) WHERE w.text_uthmani IN $divine_names WITH place, w.text_uthmani AS divine_name, count(DISTINCT v) AS name_occurrences RETURN place, divine_name, name_occurrences ORDER BY place, name_occurrences DESC

7. Narrative Structure Analysis

Description

Analyzes the structure of narrative passages in the Quran, identifying patterns in storytelling and narrative elements.

Query

// Find verses related to prophetic narratives
MATCH (t:Topic {name: "Prophets"})<-[:ADDRESSES_TOPIC]-(v:Verse)

// Identify specific prophets mentioned
MATCH (v)-[:ADDRESSES_TOPIC]->(prophet:Topic)
WHERE prophet.name IN $prophet_names

// Identify narrative elements
MATCH (v)-[:ADDRESSES_TOPIC]->(element:Topic)
WHERE element.name IN ["Birth", "Childhood", "Mission", "Challenges", "Resolution", "Lessons"]

// Group by prophet and analyze narrative structure
WITH prophet.name AS prophet_name,
     collect(DISTINCT {verse: v.verse_key, element: element.name, position: v.verse_number}) AS narrative_elements
ORDER BY prophet_name

// Organize narrative elements by position
WITH prophet_name,
     apoc.coll.sortMulti(narrative_elements, ["position"]) AS sorted_elements

// Extract narrative structure
RETURN prophet_name,
       [elem IN sorted_elements | elem.element] AS narrative_structure,
       [elem IN sorted_elements | elem.verse] AS narrative_verses

Parameters

prophet_names: List of prophets to analyze (e.g., [“Noah”, “Abraham”, “Moses”, “Jesus”])

Expected Results

A table showing narrative structures for different prophetic stories:

prophet_name	narrative_structure	narrative_verses
Abraham	[“Birth”, “Childhood”, “Mission”, “Challenges”, “Challenges”, “Resolution”, “Lessons”]	[“19:41”, “21:51”, “21:52”, “21:57”, “37:97”, “37:108”, “60:4”]
Moses	[“Birth”, “Childhood”, “Mission”, “Challenges”, “Challenges”, “Resolution”, “Lessons”]	[“28:4”, “28:13”, “20:13”, “20:17”, “26:63”, “28:40”, “28:43”]
…	…	…

Variations

Compare narrative lengths: WITH prophet_name, size(narrative_elements) AS narrative_length RETURN prophet_name, narrative_length ORDER BY narrative_length DESC
Analyze narrative element distribution: UNWIND narrative_elements AS element WITH element.element AS narrative_element, count(*) AS frequency RETURN narrative_element, frequency ORDER BY frequency DESC
Identify common narrative patterns: WITH collect(narrative_structure) AS all_structures UNWIND all_structures AS structure WITH structure, count(*) AS frequency WHERE frequency > 1 RETURN structure, frequency ORDER BY frequency DESC

8. Contextual Interpretation Analysis

Description

Analyzes how the interpretation of specific concepts varies based on their textual and thematic context.

Query

// Find verses containing a specific concept
MATCH (v:Verse)-[:CONTAINS_WORD]->(w:Word)-[:HAS_ROOT]->(r:Root {text_clean: $concept_root})

// Get thematic context
MATCH (v)-[:ADDRESSES_TOPIC]->(t:Topic)
WHERE NOT t.name IN ["Prophets", "Stories", "Parables"] // Exclude general categories

// Get textual context (surrounding words)
MATCH (v)-[:CONTAINS_WORD]->(context_word:Word)
WHERE abs(context_word.position - w.position) <= 3 AND context_word <> w

// Group by thematic context
WITH t.name AS theme,
     collect(DISTINCT v.verse_key) AS verses,
     collect(DISTINCT context_word.text_uthmani) AS contextual_words

// Calculate contextual statistics
WITH theme, verses, contextual_words,
     size(verses) AS verse_count,
     size(contextual_words) AS vocabulary_size

// Return contextual analysis
RETURN theme,
       verse_count,
       vocabulary_size,
       verses[0..3] AS sample_verses,
       contextual_words[0..10] AS common_context
ORDER BY verse_count DESC
LIMIT $limit

Parameters

concept_root: Root word representing the concept to analyze (e.g., “عدل” for justice)
limit: Maximum number of thematic contexts to return (e.g., 15)

Expected Results

A table showing how the concept is used in different thematic contexts:

theme	verse_count	vocabulary_size	sample_verses	common_context
Law	28	156	[“4:58”, “5:8”, “5:42”]	[“حكم”, “قسط”, “شهد”, “حق”, “ميزان”, “قضى”, “ناس”, “بين”]
Faith	15	89	[“16:90”, “7:29”, “57:25”]	[“أمر”, “قسط”, “دين”, “رب”, “حق”, “أقام”]
…	…	…	…	…

Variations

Analyze semantic variation: MATCH (v:Verse)-[:CONTAINS_WORD]->(w:Word)-[:HAS_ROOT]->(r:Root {text_clean: $concept_root}) WITH v, w, avg(v.embedding) OVER (PARTITION BY t.name) AS theme_centroid, v.embedding AS verse_embedding RETURN t.name AS theme, vector_similarity(theme_centroid, verse_embedding) AS semantic_coherence
Compare with translation variations: MATCH (v)-[:HAS_TRANSLATION]->(trans:Translation {language_name: "English"}) WITH theme, collect(DISTINCT trans.text) AS translations RETURN theme, [word IN apoc.text.keywords(apoc.text.join(translations, " "), {minSize: 4}) WHERE NOT word IN $stop_words | word] AS translation_keywords
Identify contextual antonyms: MATCH (v)-[:CONTAINS_WORD]->(opposite:Word)-[:HAS_ROOT]->(opp_root:Root) WHERE opp_root.text_clean IN $opposite_roots WITH theme, opp_root.text_clean AS opposite_concept, count(DISTINCT v) AS co_occurrences RETURN theme, collect({concept: opposite_concept, count: co_occurrences}) AS opposing_concepts

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Documentation Index

​Advanced Analytical Queries

​1. Multi-dimensional Thematic Analysis

​Description

​Query

​Parameters

​Expected Results

​Variations

​2. Chronological Theme Evolution Analysis

​Description

​Query

​Parameters

​Expected Results

​Variations

​3. Structural Pattern Analysis

​Description

​Query

​Parameters

​Expected Results

​Variations

​4. Semantic Coherence Analysis

​Description

​Query

​Parameters

​Expected Results

​Variations

​5. Network Analysis of Thematic Relationships

​Description

​Query

​Parameters

​Expected Results

​Variations

​6. Comparative Analysis of Divine Attributes

​Description

​Query

​Parameters

​Expected Results

​Variations

​7. Narrative Structure Analysis

​Description

​Query

​Parameters

​Expected Results

​Variations

​8. Contextual Interpretation Analysis

​Description

​Query

​Parameters

​Expected Results

​Variations

Advanced Analytical Queries

1. Multi-dimensional Thematic Analysis

Description

Query

Parameters

Expected Results

Variations

2. Chronological Theme Evolution Analysis

Description

Query

Parameters

Expected Results

Variations

3. Structural Pattern Analysis

Description

Query

Parameters

Expected Results

Variations

4. Semantic Coherence Analysis

Description

Query

Parameters

Expected Results

Variations

5. Network Analysis of Thematic Relationships

Description

Query

Parameters

Expected Results

Variations

6. Comparative Analysis of Divine Attributes

Description

Query

Parameters

Expected Results

Variations

7. Narrative Structure Analysis

Description

Query

Parameters

Expected Results

Variations

8. Contextual Interpretation Analysis

Description

Query

Parameters

Expected Results

Variations