AR-Sanad 280K: A Novel 280K Artificial Sanads Dataset for Hadith Narrator Disambiguation

Abstract

Determining hadith authenticity is vitally important in the Islamic religion because hadiths record the sayings and actions of Prophet Muhammad (PBUH), and they are the second source of Islamic teachings following the Quran. When authenticating a hadith, the reliability of the hadith narrators is a big factor that hadith scholars consider. However, many narrators share similar names, and the narrators’ full names are not usually included in the narration chains of hadiths. Thus, first, ambiguous narrators need to be identified. Then, their reliability level can be determined. There are no available datasets that could help address this problem of identifying narrators. Here, we present a new dataset that contains narration chains (sanads) with identified narrators. The AR-Sanad 280K dataset has around 280K artificial sanads and could be used to identify 18,298 narrators. After creating the AR-Sanad 280K dataset, we address the narrator disambiguation in several experimental setups. The hadith narrator disambiguation is modeled as a multiclass classification problem with 18,298 class labels. We test different representations and models in our experiments. The best results were achieved by finetuning BERT-Based deep learning model (AraBERT). We obtained a 92.9 Micro F1 score and 30.2 sanad error rate (SER) on the validation set of our artificial sanads AR-Sanad 280K dataset. Furthermore, we extracted a real test set from the sanads of the famous six books in Islamic hadith. We evaluated the best model on the real test data, and we achieved 83.5 Micro F1 score and 60.6 sanad error rate.

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

“…Hadith is one of the main law resources of Muslim society. In the Arabic language, hadith means speech or talk [1] . Thus, in Islamic terminology, hadith consists of words, deeds, and all the circumstances of the Prophet Muhammad, peace be upon him (PBUH).…”

“…Thus, in Islamic terminology, hadith consists of words, deeds, and all the circumstances of the Prophet Muhammad, peace be upon him (PBUH). Hadith can also refer to Prophet Muhammad's (PBUH) approval or criticism of the actions of other people [1] . Many researches discussed hadith's Matan (redaction) rather than sanad (a series of narrators).…”

“… Import data from .json file fromJSON("[original file]") Separation process sep.data <- strsplit(as.character([imported data]$id), split = "#", fixed = TRUE[ [1] ]) unlist(sep.data, use.names = T) sep.data sep.data = as.data.frame(sep.data) Variable transformation data.frame(lapply(sep.data, "length<-", max(lengths(sep.data)))) Extract sanad data from the matan function(i){ gsub("[\\[\\]]", "", regmatches([transformed data][i,3], gregexpr("(?<=\\[).*? (?=\\])", [transformed data][i,3], perl = T))[ [1] ]) } Clean hadith narrators data (example for removing personal pronouns) data.frame(lapply([extracted data], gsub, pattern="radliyallahuanhu", replacement='')) Data formatting rotate <- function(x) t(apply(x, 1, rev)) rawi. [book name] <- rotate([cleaned data]) rawi.…”

“…[book name]) id. [book name] <- rep(1:dim([cleaned data]) [1] ,each=[maximal number of narrators]) id.rawi. [book name] <- c(rep(1:[maximal number of narrators],dim([cleaned data]) [1] )) size.…”

“…[book name] <- rep(1:dim([cleaned data]) [1] ,each=[maximal number of narrators]) id.rawi. [book name] <- c(rep(1:[maximal number of narrators],dim([cleaned data]) [1] )) size. [book name] <- c(rep(1,dim([cleaned data]) [1] *dim([cleaned data]) [2] )) [book name] <- rbind(id.…”