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Documentation Index

Fetch the complete documentation index at: https://docs.superquran.com/llms.txt

Use this file to discover all available pages before exploring further.

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Quran Knowledge Graph Schema

The schema of the Quran Knowledge Graph defines how different types of information are structured and connected. This page explains the schema in both non-technical and technical terms.

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Schema Overview

Schema overview diagram The Quran Knowledge Graph contains several types of nodes (entities) and relationships that connect them. This structure allows for rich, interconnected representation of the Quranic text and related knowledge.

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Node Types (Entities)

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Surah (Chapter)

Surahs are the 114 chapters of the Quran. Properties include:
  • Surah number (1-114)
  • Name in Arabic and English
  • Revelation place (Mecca or Medina)
  • Revelation order
  • Number of verses
  • Brief description

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Verse

Verses (ayat) are the individual units of revelation within each surah. Properties include:
  • Verse key (e.g., “2:255” for Surah 2, Verse 255)
  • Arabic text (Uthmani script)
  • Position information (juz, hizb, ruku)
  • Revelation context
  • Sajdah indication (whether prostration is recommended)
  • Vector embedding (for semantic search)

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Word

Words are the individual Arabic words within each verse. Properties include:
  • Arabic text
  • Position in verse
  • Grammatical information (part of speech, gender, number)
  • Vector embedding

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Root Word

Root words are the linguistic roots from which Arabic words are derived. Properties include:
  • Arabic text (typically three consonants)
  • Basic meaning
  • Derived forms

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Topic

Topics represent thematic elements addressed in the Quran. Properties include:
  • Name
  • Description
  • Related concepts
  • Vector embedding

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Tafsir

Tafsir entries are scholarly interpretations of verses. Properties include:
  • Author/source
  • Text
  • Time period
  • Methodology (e.g., linguistic, legal, spiritual)

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Translation

Translations are verse renderings in different languages. Properties include:
  • Language
  • Translator
  • Text
  • Year
  • Approach (literal vs. interpretive)

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Relationship Types

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CONTAINS

Connects a container to what it contains:
  • Surah CONTAINS Verse
  • Verse CONTAINS Word

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HAS_ROOT

Connects a word to its linguistic root:
  • Word HAS_ROOT RootWord

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ADDRESSES_TOPIC

Connects a verse to topics it addresses:
  • Verse ADDRESSES_TOPIC Topic
  • With properties like relevance score

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HAS_TAFSIR

Connects a verse to its interpretations:
  • Verse HAS_TAFSIR Tafsir

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HAS_TRANSLATION

Connects a verse to its translations:
  • Verse HAS_TRANSLATION Translation

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SIMILAR_TO

Connects semantically similar verses:
  • Verse SIMILAR_TO Verse
  • With properties like similarity score and similarity type

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REFERENCES

Connects verses that reference each other:
  • Verse REFERENCES Verse
  • With properties like reference type

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Schema Visualization

Detailed schema diagram

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Non-Technical Explanation

Think of the schema as a blueprint for how information is organized:
  • Surahs are like books containing verses
  • Verses contain words, which come from root words
  • Verses address various topics
  • Scholars provide interpretations (tafsir) of verses
  • Translators create translations of verses in different languages
  • Some verses are similar to or reference other verses
This organization allows you to navigate the Quran in many ways:
  • Follow the traditional chapter and verse structure
  • Explore by topic or theme
  • Trace linguistic connections through root words
  • Compare different interpretations and translations
  • Discover verses with similar meanings

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Technical Details

For developers and technical users, here’s the graph schema definition in Cypher: 
// Node definitions
CREATE NODE TABLE Surah (
  surah_number INTEGER PRIMARY KEY,
  name_arabic TEXT,
  name_english TEXT,
  revelation_place TEXT,
  revelation_order INTEGER,
  verse_count INTEGER,
  description TEXT
);

CREATE NODE TABLE Verse (
  verse_key TEXT PRIMARY KEY,
  surah_number INTEGER,
  verse_number INTEGER,
  text_uthmani TEXT,
  text_simple TEXT,
  juz INTEGER,
  hizb INTEGER,
  ruku INTEGER,
  sajdah BOOLEAN,
  embedding VECTOR<FLOAT, 768>,
  FOREIGN KEY (surah_number) REFERENCES Surah(surah_number)
);

CREATE NODE TABLE Word (
  word_id INTEGER PRIMARY KEY,
  verse_key TEXT,
  position INTEGER,
  text_uthmani TEXT,
  text_simple TEXT,
  part_of_speech TEXT,
  embedding VECTOR<FLOAT, 768>,
  FOREIGN KEY (verse_key) REFERENCES Verse(verse_key)
);

CREATE NODE TABLE RootWord (
  root_id INTEGER PRIMARY KEY,
  text_arabic TEXT,
  meaning_english TEXT
);

CREATE NODE TABLE Topic (
  topic_id INTEGER PRIMARY KEY,
  name TEXT,
  description TEXT,
  embedding VECTOR<FLOAT, 768>
);

CREATE NODE TABLE Tafsir (
  tafsir_id INTEGER PRIMARY KEY,
  verse_key TEXT,
  author TEXT,
  text TEXT,
  FOREIGN KEY (verse_key) REFERENCES Verse(verse_key)
);

CREATE NODE TABLE Translation (
  translation_id INTEGER PRIMARY KEY,
  verse_key TEXT,
  language TEXT,
  translator TEXT,
  text TEXT,
  FOREIGN KEY (verse_key) REFERENCES Verse(verse_key)
);

// Relationship definitions
CREATE REL TABLE CONTAINS (
  FROM Surah TO Verse,
  order_index INTEGER
);

CREATE REL TABLE HAS_WORD (
  FROM Verse TO Word,
  position INTEGER
);

CREATE REL TABLE HAS_ROOT (
  FROM Word TO RootWord
);

CREATE REL TABLE ADDRESSES_TOPIC (
  FROM Verse TO Topic,
  relevance FLOAT
);

CREATE REL TABLE HAS_TAFSIR (
  FROM Verse TO Tafsir,
  source TEXT
);

CREATE REL TABLE HAS_TRANSLATION (
  FROM Verse TO Translation,
  type TEXT
);

CREATE REL TABLE SIMILAR_TO (
  FROM Verse TO Verse,
  similarity_score FLOAT,
  similarity_type TEXT
);

CREATE REL TABLE REFERENCES (
  FROM Verse TO Verse,
  reference_type TEXT
);

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Schema Evolution

The schema evolves over time as we add new data sources and capabilities. Recent and planned additions include:
  • Hadith connections to verses
  • Historical context nodes
  • Linguistic feature nodes (rhetorical devices, grammar patterns)
  • User annotation capabilities
For more technical details about working with the schema, see the Graph Database Implementation page.