A Rich Arabic WordNet Resource for Al-Hadith Al-Shareef

Alkhatib, Manar; Monem, Azza Abdel; Shaalan, Khaled

doi:10.1016/j.procs.2017.10.098

Cited by 22 publications

(8 citation statements)

References 7 publications

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“…The prophetic hadith contains a main two parts the Matn (hadith text) and the Sanad (narrator of hadith) [16], [14]. The Sanad of hadith has been ignored, and we focused on the preprocessing of text.…”

Section: B Hadith Text Pre-processingmentioning

confidence: 99%

Classification of Hadith According to Its Content Based on Supervised Learning Algorithms

2019

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Given the importance of the Prophet's Hadith for Muslims all over the world, where it is the second source of Islam after the Qur'an and the fundamental resource of legislation in the Islam community. This study is focused on the Classification of hadith automatically into different categories according to its content, based on Hadith text. The objective of this study is to build a classifier model can classify and differentiate hadith categories, to predict its topic like prayer, fasting, and zakat; using data mining and machine learning techniques. In this study, many supervised learning algorithms plus combination methods such as the stacking algorithm was used to improve classification accuracy. The best three classifiers were evaluated mainly: the Decision Tree (DT), Random Forest (RF), and Naïve Bayes (NB), which achieved higher accuracy reached up to 0.965%, 0.956, and 0.951% respectively. Also, Binary (Boolean algebra) and TF-IDF methods as term weighting was applied to determine the frequency of each word in the hadith text, and identify the most significant features in training dataset using Information Gain (IG), and Chi-square (CHI). The experimental results showed that retrain these classifiers after applying IG and CHI as features selection; gave better accuracy compared to the previous results. Additional to, the best classifier gave high accuracy was DT, it has achieved higher accuracy in most test cases whether in the Boolean algebra or TF-IDF because it can deal with missing values and identifying the most essential features from the training dataset, known as features engineering.

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Section: B Hadith Text Pre-processingmentioning

confidence: 99%

Classification of Hadith According to Its Content Based on Supervised Learning Algorithms

2019

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“…Alkhatib et al [11] proposed a new approach to create Al-Hadith WordNet, a useful language tool specifically tailored for hadith texts. Unlike the Modern Standard Arabic (MSA) WordNet, which exists, there wasn't a similar resource for the Classical Arabic found in hadith texts.…”

Section: B Wordnetmentioning

confidence: 99%

Arabic Ontology for Hadith texts - A survey

Muhammed,

Azab,

Ali

et al. 2024

The Egyptian Journal of Language Engineering

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Hadith texts play a significant role in Islamic scholarship, providing guidance for Muslims in understanding and practicing Islam. This paper explores the importance of Hadith texts, their categorization based on reliability, and the structure of a typical Hadith. It discusses the use of software tools and ontologies in analysing Hadith texts. The paper also examines the Arabic language, particularly its complexities and the challenges it poses in Natural Language Processing tasks. Arabic is classified into Classical, Modern, and dialectal forms. Arabic has many challenges that affect the building of ontologies; for instance, the lack of linguistic resources and complex morphology. The paper also explores the development of ontologies in Islamic studies, specifically for Hadith, highlighting their unique characteristics and applications. Several research works related to Hadith ontology and its potential applications are discussed, including the creation of Al-Hadith WordNet, computational analysis of Hadith texts, and the development of ontology-based systems for authentication and retrieval of Hadith information.

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“…Results demonstrated that their calculation is better regarding accuracy much of the time of various word lengths in examination with the other two Arabic stemmers. Alkhatib et al (2017) proposed another algorithm, which is called a novel methodology for building up Al-Hadith Al-Shareef WordNet linguistic to fills its needs for various tasks of Arabic natural language processing. Particularly, they build up semantic associations between words in order to achieve a decent comprehension of the implications of the words in Al-Hadith.…”

Section: Root Extraction Algorithmmentioning

confidence: 99%

Text mining: A survey of Arabic root extraction algorithms

al.¹

2021

Int. j. adv. appl. sci.

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In all Arab countries, the Arabic language is the official language spoken and written and is one of the oldest known languages. This paper aims to explain and discuss the work done on extracting the root of the Arabic word and Stemming algorithms. Text mining has become of interest to scientists, researchers, and users because of the existence of big data and deep learning algorithms that can analyze giant sets of unstructured data. The basic algorithms are used to extract and classify texts, information retrieval systems, and indexes. Algorithms are used to extract the root of a word from different natural languages. This paper will present a brief background and comprehensive presentation of a number of algorithms that handle the Arabic text to extract the word root in its light, heavy, hybrid, leading, and Markovian form. There are a number of papers, articles, and research papers that deal with extracting the Arabic root from the word. This paper will present a brief background for a number of stemming algorithms on how to extracting the root and stem of the Arabic word, then make a comparison and discussion of a number of selected algorithms in terms of accuracy, data set, method of stemming regarding of strengths and weakness.

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A Rich Arabic WordNet Resource for Al-Hadith Al-Shareef

Cited by 22 publications

References 7 publications

Classification of Hadith According to Its Content Based on Supervised Learning Algorithms

Classification of Hadith According to Its Content Based on Supervised Learning Algorithms

Arabic Ontology for Hadith texts - A survey

Text mining: A survey of Arabic root extraction algorithms

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