A Novel Hadith Processing Approach Based on Genetic Algorithms

Abstract

Quran and Hadith are the main religious sources in Islam. Hadith is mainly the saying of Prophet Mohammad, and it consists of two parts: first is ''Isnad''; which is the series of persons (narrators) who report (narrate) Hadith, and the second is ''Matn''; which is the saying or the narration itself. Isnad can be divided into phrases called ''Isnad-Phrases'', which could be tagged according to proposed Part-Of-Isnads (POIs) that represent the entity types of Isnad, such as Narrator Name, Prophet Name and Received Method. One of the main objectives of Hadith sciences is to ascertain the validity of Hadith and determine if it is accepted or rejected; this is known as Hadith judgment. Hadith scholars put many rules to judge Hadiths; some of these rules are related to the chain of narrators. Therefore, to judge Hadith, first, the narrators' names in Isnad should be extracted then judgment rules can be applied. Many researches proposed different methods to extract narrators' names from Isnad. To the best of the author's knowledge, there is no research process Isnad using Genetic Algorithms (GA). In this research, a novel approach is introduced for Isnad processing based on GA. This approach aims to predict the narrators' names and the other POIs for Isnad. The experiments, which conducted on all Hadiths narrated in ''Sahih Muslim'' book, show that the proposed approach achieved 81.44% accuracy. For further research in this genre, investigation of utilizing other approaches for Isnad processing, such as deep learning, is recommended. INDEX TERMS Arabic natural language processing, genetic algorithms, hadith, isnad, tagging.

Exploring the relationship between hadith narrators in Book of Bukhari through SPADE algorithm

“…Another similar study used a finite-state machine to construct the narration tree [7] . Other studies were also conducted using a variety of methods, such as using Principal Component Analysis (PCA) and Backpropagation Neural Network (BPNN) [8] , Support Vector Machine (SVM), Naïve Bayes and K-Nearest Neighbor (K-NN) [9] , and Genetic Algorithm (GA) [10] . According to the importance of the hadith sanad, a study on the relationship between one narrator and another is important because it relates to the continuity of the sanad in a hadith.…”

A Hidden Markov Model-Based Tagging Approach for Arabic Isnads of Hadiths

“…e experiments were conducted on 38,102 Hadiths from the books of Tirmzi, Nasai, Malik, Ibnu Majah, Darimi, Ahmad, Al-Bukhari, Muslim, and Abu Dawud, and the F-measure was 86%. Najeeb [4] suggested a new method to process Hadiths built on genetic algorithms (GAs), and this method seeks to foretell narrators' names of Hadiths and the rest part of Isnads (POIs) as well. e experiments were conducted on 3,033 from the Sahih Muslim book, and the accuracy was 81.44%.…”

“…)." is Isnad might be separated into words and phrases called "Isnad-Phrases," every phrase might be categorized under one of the following part of Isnads (POIs): Prophet-Name, Narrator-Name, Narrator-Name-Prefix, Received-Method, Received-Method-Prefix, Replacement, Title, and Others [4]. Table 1 shows the POIs of Hadith1.…”

Towards a Deep Leaning-based Approach for Hadith Classification

“…Numerous studies using computing methods presented in the literature that aim to help Arabic Natural Language Processing (ANLP); Najeeb et al [6] show to their efforts in initiation an ANLP technical lab in Al-Qunfudah college, Umm Al-Qura University, Saudi Arabia, to assist ANLP and Islamic sciences. In [7], Najeeb proposed a novel Hadith processing approach based on Genetic Algorithms (GA), the approach aims to predict storytellers' names and the other Part-Of-Isnads (POIs) for Hadiths. In [8], Najeeb presented an XML database for storytellers and Hadiths.…”