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QurSci-Onto: A Hierarchical Ontology and Dataset for Scientific Exegesis in the Quran

Paper · 2026 · Ibad-ur-Rehman Rashid, Junaid Hussain and Sadam Al-Azani

Summary

Computational resources for the Quran usually store verses as flat text. This discards the ordering and cause-and-effect links that scientific exegesis (Tafsir Ilmi) attributes to phenomena such as embryonic development or the water cycle.

The authors build QurSci-Onto, a resource with three layers:

  • Ayah Ontology: 194 scientifically relevant verses, each with Arabic text, an English translation, broad categories and topics.
  • Scientific Tafsir Index: 260 exegetical records that point to exact pages in two exegesis works.
  • Scientific Ontology: 8 topics broken into 73 nodes.

Each topic is modelled either as a process, with ordered stages joined by relations like transforms_into, or as a static entity built around a central root concept. The ontology was extracted with LLM assistance and then checked by experts. Fine-grained node mappings cover only a subset of verses.

In a small retrieval experiment, adding ontology-derived keywords and summaries to verses improved NDCG in most categories. The resource offers citation-backed grounding for Arabic RAG and QA, a domain where hallucinated answers are unacceptable.

Key points

  • Sources: verses were selected from the Ayah Index of Tafsir al-Ayat al-Kawniyah (El-Naggar, 2001; 4 volumes). Exegesis is also linked from I'jaz al-'Ilmi fi al-Quran (1 volume). English translations are by Mustafa Khattab.

  • Schema: cross-layer pointers (hasTafsirID, scientific topic/node IDs) connect the layers. Nodes are typed as Process (stages ordered via LogicalOrder) or Entity. Example: the Big Bang sequence runs Ratq → Fatq → Musi'un.

  • Retrieval results (NDCG, baseline → ontology-enhanced):

    CategoryBaselineEnhancedGain
    Oceanography0.5410.775+43.1%
    Embryology0.5010.660+31.9%
    Hydrology0.6560.838+27.7%
    Cosmology0.7330.908+23.8%
    General——+7.2%
    Bee Biology1.0001.000no change

  • Limitations: each category has only 3–5 queries. The abstract claims support for multi-hop reasoning, but the authors' own limitations section says the evaluation does not yet exploit the ontology's relational structure for this.

  • Release: the dataset, ontology files and retrieval code are on GitHub (Ebad-urRehman/QurSci-Onto).

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓King Fahd University of Petroleum and MineralsDhahran · Saudi Arabia52 works · 4,666 citations

Also on the byline: Hazara University.

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksSocial Sciences · 1,772 worksBiochemistry, Genetics and Molecular Biology · 234 worksMolecular Biology · 231 worksEducation · 203 works

🧭 Topics 2Research subjects and languages the work is about.Semitic languages · 2,908 worksKnowledge graphs & ontologies · 192 works

Cite

Cite as Ibad-ur-Rehman Rashid, Junaid Hussain and Sadam Al-Azani. 2026. QurSci-Onto: A Hierarchical Ontology and Dataset for Scientific Exegesis in the Quran. Proceedings of the 2nd Workshop on NLP for Languages Using Arabic Script.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.abjadnlp-1.22

@inproceedings{rashid2026qursci,
  title = {{QurSci-Onto: A Hierarchical Ontology and Dataset for Scientific Exegesis in the Quran}},
  author = {Ibad-ur-Rehman Rashid, Junaid Hussain and Sadam Al-Azani},
  booktitle = {Proceedings of the 2nd Workshop on NLP for Languages Using Arabic Script},
  year = {2026},
  doi = {10.18653/v1/2026.abjadnlp-1.22},
  url = {https://doi.org/10.18653/v1/2026.abjadnlp-1.22}
}

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