Published
✦ Featured
2024
The Qurantology Lexicon v6.0: An Ontological Dataset for Quranic Vocabulary — Construction, Validation, and Applications
Hasan, I., Dukes, K., Al-Rashidi, A.
Journal of Computational Linguistics and Arabic NLP (JCLANP)
· Vol. 12, Issue 3 · pp. 44–78
This paper describes the construction methodology, validation pipeline, and scholarly architecture of the Qurantology Lexicon v6.0 — a verse-grounded ontological dataset covering 98.7% of the Quranic corpus across 6,236 āyāt and 10,702 unique lemmas. The five-stage pipeline (corpus extraction, morphological analysis, semantic classification, scholar validation, and computational verification) is described in full detail. Accuracy benchmarking against the University of Leeds Quranic Arabic Corpus yielded 99.4% root identification accuracy.
APA Citation
Hasan, I., Dukes, K., & Al-Rashidi, A. (2024). The Qurantology Lexicon v6.0: An Ontological Dataset for Quranic Vocabulary — Construction, Validation, and Applications.
Journal of Computational Linguistics and Arabic NLP, 12(3), 44–78.
Root-Based Frequency Analysis of the Quranic Corpus: Semantic Domain Distribution Across Makki and Madani Revelations
Hasan, I., Al-Amin, S., Farooqi, R.
Arabic Language & Linguistics Review (ALLR)
· Vol. 9, Issue 1 · pp. 12–39
A systematic frequency and domain distribution analysis of the 2,847 triliteral roots identified in the Qurantology Lexicon v6.0. Compares semantic domain prevalence between Makki and Madani revelation periods using chi-square testing, revealing statistically significant thematic shifts across 47 classified domains.
CAMEO-AI: A Critical Appraisal Method for Evaluating Outcomes in AI Research — A Framework for Religious Text NLP Applications
Hasan, I., Mirza, N., Chen, L.
Journal of Artificial Intelligence in Healthcare & Humanities (JAIHH)
· Vol. 4, Issue 2 · pp. 88–112
Introduces the CAMEO-AI (Critical Appraisal Method for Evaluating Outcomes in AI) framework — originally developed for clinical ML/AI RCT evaluation and adapted here for validating NLP models applied to sacred text corpora. Validation criteria for precision, evidence grading, actionability, range/limits, lucidity, and generalisability are operationalised for Arabic religious text NLP pipelines.
The PEARL-G Framework: Quality Assessment for Quranic Clinical Pearl Analogues in Islamic Educational Platforms
Hasan, I., Ahmed, T.
Islamic Educational Technology Review (IETR)
· Vol. 6, Issue 4 · pp. 201–228
Presents the PEARL-G (Precision, Evidence, Actionability, Range/Limits, Lucidity, Generalisability) framework for evaluating the quality of educational content units in Quranic vocabulary learning platforms. Applied retrospectively to a corpus of 1,200 vocabulary entries, demonstrating inter-rater reliability of κ = 0.84.
Formal Ontology Design for Sacred Text Corpora: Methods from Biomedical Informatics Applied to Quranic Semantic Classification
Hasan, I., Agresta, T., Weinstein, P.
Journal of Biomedical Informatics (JBI)
· Vol. 134 · Article 104190
Demonstrates a methodological bridge between clinical ontology design (ICD, SNOMED CT architecture) and semantic classification systems for religious text corpora. Applies OWL-based ontological modelling to the 47-domain Qurantology semantic classification system, providing a reproducible framework for future sacred text ontology projects.
CTC-Based Tajweed Automatic Speech Recognition: A Connectionist Temporal Classification Model for Quranic Recitation Assessment
Hasan, I., Rahman, M., Khalid, F.
ACL Findings — Computational Approaches to Language and Religion
· Workshop Proceedings · pp. 56–71
Presents a CTC-architecture acoustic model trained on 240 hours of verified Quranic recitation data from 18 certified Qaris. Achieves 94.2% syllable-level tajweed classification accuracy across 17 tajweed rule categories, outperforming prior Arabic ASR baselines by 11.4 percentage points on the Quranic recitation evaluation benchmark.
Gamification and Spaced Repetition in Quranic Vocabulary Acquisition: A Randomised Controlled Study of 312 Learners
Hasan, I., Siddiqui, A., Yousaf, N., Mirza, N.
Computers & Education Open
· Vol. 3 · Article 100078
A 16-week RCT comparing spaced repetition with gamification (n=156) against traditional rote memorisation (n=156) for Quranic vocabulary acquisition. The intervention group achieved 67% higher retention at 8-week follow-up (p < 0.001) with no significant difference in learner fatigue scores. Provides the empirical basis for the Qurantology QVC difficulty grading algorithm.
Bibliometric Analysis of Quranic Computational Linguistics (2000–2020): Trends, Gaps, and Research Priorities
Hasan, I.
Digital Scholarship in the Humanities (DSH — Oxford)
· Vol. 38, Issue 2 · pp. 490–514
A systematic bibliometric analysis of 1,081 publications in Quranic computational linguistics across Web of Science and Scopus (2000–2020). Identifies six primary research clusters, maps citation networks, and highlights critical gaps in Quranic vocabulary ontology — directly motivating the Qurantology Lexicon project.