TELK
OMNIKA
T
elecommunication,
Computing,Electr
onics
and
Contr
ol
V
ol.
24,
No.
3,
June
2026,
pp.
840
∼
851
ISSN:
1693-6930,
DOI:
10.12928/TELK
OMNIKA.v24i3.27447
❒
840
Computational
methodologies
f
or
sanad-based
hadith
analysis:
a
r
e
view
Abdelilah
Mhamedi,
Mohammed
Mghari,
Abdelaaziz
El
Hibaoui
Department
of
Computer
Science,
F
aculty
of
Science,
Abdelmalek
Essa
ˆ
adi
Uni
v
ersity
,
T
´
etouan,
Morocco
Article
Inf
o
Article
history:
Recei
v
ed
Aug
2,
2025
Re
vised
Dec
11,
2025
Accepted
Jan
30,
2026
K
eyw
ords:
Authenticity
Hadith
Machine
learning
Natural
language
processing
Netw
ork
analysis
Ontology
Sanad
ABSTRA
CT
Hadith
literature,
a
cornerstone
of
Islamic
tradition,
critically
depends
on
the
sanad
(chain
of
narrators)
for
authentication,
a
process
traditionally
requiring
profound
scholarly
e
xpertise.
This
paper
presents
a
systematic
re
vie
w
of
com-
putational
methodologies
designed
to
enhance
and
automate
sanad
analysis,
bridging
Islamic
studies
with
adv
anced
articial
intelligence
(AI).
W
e
cate
gorize
progress
across
four
k
e
y
domains:
automated
authenticity
classication,
sophis-
ticated
narrator
netw
ork
analysis,
te
xtual
information
e
xtraction
(e.g.,
named
entity
recognition),
and
the
de
v
elopment
of
specialized
datasets
and
ontologies.
Our
ndi
ngs
re
v
eal
a
signicant
paradigm
shift
from
rule-based
systems
to
ad-
v
anced
machine
lear
ning
(ML)
and
deep
learni
ng
(DL)
techniques.
This
re
vie
w
synthesizes
contrib
utions
from
o
v
er
50
studies,
highlighting
critical
challenges
including
data
scarci
ty
,
narrator
disambiguation,
and
cross-linguistic
resource
limitations.
W
e
emphasize
the
no
v
elty
of
this
cross-domain
synthesis
and
dis-
cuss
ho
w
these
intelligent
systems
can
be
inte
grat
ed
into
digital
Islamic
archi
v
es,
lo
w-resource
mobile
hadith
applications,
and
embedded
natural
language
pro-
cessing
(NLP)
engines.
This
w
ork
charts
a
course
for
future
research
to
de
v
elop
more
rob
ust,
scalable,
and
ethically
grounded
computational
tools,
complement-
ing
traditional
hadith
scholarship
with
adv
anced
engineering
solutions.
This
is
an
open
access
article
under
the
CC
BY
-SA
license
.
Corresponding
A
uthor:
Mohammed
Mghari
Department
of
Computer
Science,
F
aculty
of
Science,
Abdelmalek
Essa
ˆ
adi
Uni
v
ersity
P
.O.
Box.
2121,
M’Hannech
II,
T
´
etouan,
93030,
Morocco
Email:
mohammed.mghari@uae.ac.ma
1.
INTR
ODUCTION
Hadith
literature,
which
comprises
narrations
of
the
sayings,
actions,
and
appro
v
als
of
the
Prophet
Muhammad
peace
be
upon
him
(PB
UH),
is
a
foundational
source
of
Islamic
teachings
and
la
w
,
second
only
to
the
Qur’an
[1],
[2].
Ev
ery
hadith
is
composed
of
tw
o
primary
parts:
the
Matn
,
which
is
the
te
xt
of
the
narration
itself,
and
the
sanad
(also
kno
wn
as
Isnad
),
which
is
the
chain
of
narrators
responsible
for
transmitting
the
Matn
[3].
The
authenticity
of
a
hadith
is
of
utmost
importance,
as
it
directly
inuences
Islamic
jurisprudence,
theology
,
and
daily
practice.
F
or
centuries,
hadith
scholars
(
muhaddithin
)
ha
v
e
emplo
yed
a
rigorous
science
of
hadith
criticism
(
mustalah
al-hadith
)
to
v
erify
the
reliability
of
these
narrations.
A
central
pillar
of
this
science
is
the
meticulous
analysis
of
the
sanad
[4],
[5].
This
process
in
v
olv
es
a
deep
e
v
aluation
of
the
biographical
details
of
each
narrator
in
the
chain
(
’ilm
al-rijal
)
to
assess
their
inte
grity
,
memory
,
and
reliability
,
as
well
a
s
to
ensure
the
chain
is
continuous
and
free
from
hidden
defects
[6],
[7].
The
traditional
method
of
sanad
analysis
is
an
e
xceptionally
comple
x
and
labor
-intensi
v
e
endea
v
or
,
J
ournal
homepage:
http://journal.uad.ac.id/inde
x.php/TELK
OMNIKA
Evaluation Warning : The document was created with Spire.PDF for Python.
TELK
OMNIKA
T
elecommun
Comput
El
Control
❒
841
demanding
years
of
specialized
study
and
access
to
v
ast
biographical
and
historical
resources
[8],
[9].
The
immense
v
olum
e
of
hadi
th
literature,
which
includes
hundreds
of
thousands
of
narrations
across
numerous
collections,
presents
a
formidable
challenge
for
comprehensi
v
e
manual
analysis.
This
dif
culty
is
compounded
by
the
intricacies
of
narrator
names,
which
often
appear
in
multiple
v
ariations,
and
the
c
omple
x,
often
branching
chains
of
transmission
[10].
In
t
he
digital
era,
while
man
y
hadith
collections
are
no
w
accessible
online,
the
computational
tools
to
analyze
them
are
still
de
v
eloping
and
remain
some
what
fragmented.
There
is
a
pressing
need
to
harness
modern
technology
to
ass
ist
scholars,
automate
repetiti
v
e
analytical
tasks,
and
unco
v
er
ne
w
insights
from
the
v
ast
data
embedded
within
the
sanads
[11],
[12].
The
con
v
er
gence
of
computer
science
and
Islamic
studies
has
catalyzed
a
vibrant
ne
w
eld
dedic
ated
to
applying
computational
techniques
t
o
hadith
analysis
[13]-[15].
This
paper
of
fers
a
systematic
re
vie
w
of
contemporary
sanad-based
hadith
studies,
pro
viding
a
structured
o
v
ervie
w
of
the
current
state-of-the-art.
Re-
cent
research
demonstrates
a
clear
e
v
olution
from
early
rule-based
systems
to
the
adoption
of
machine
learning
(ML)
and,
more
recently
,
adv
anced
deep
learning
(DL)
approaches
[8],
[
1
6]
,
[17].
By
or
g
anizing
recent
con-
trib
utions
into
four
primary
cate
gories
automated
classication,
narrator
netw
ork
analysis,
te
xtual
component
e
xtraction,
and
dataset/ontology
construction
this
re
vie
w
consolidates
current
kno
wledge.
W
e
highlight
the
pro-
gression
of
methodologies,
from
foundational
e
xpert
systems
to
transformer
models
lik
e
bidirectional
encoder
representations
from
transformers
(BER
T)
[18]-[20],
and
synthesize
their
reported
ef
fecti
v
eness
and
limita-
tions.
This
re
vie
w
aims
to
answer
the
question:
”What
are
the
current
computational
methodologies
applied
to
sanad-based
hadith
analysis,
what
are
their
reported
performances
and
limitations,
and
what
are
the
promising
future
directions?”
Our
primary
focus
is
on
synthesizing
the
e
xisting
tools
and
theoretical
de
v
elopments
that
le
v
erage
computational
po
wer
to
dissect
sanad
structures,
assess
narrator
credibility
,
and
enhance
scholarly
research.
W
e
emphasize
the
no
v
elty
of
this
cross-domain
synthesis,
sho
wcasing
ho
w
intelligent
systems,
can
re
v
olutionize
hadith
studies.
Charting
a
course
for
future
inte
rdisciplinary
research
will
accelerate
the
de
v
elop-
ment
of
rob
ust
and
scalable
computational
solutions
that
complement,
rather
than
replace,
the
in
v
aluable
w
ork
of
traditional
hadith
scholarship.
2.
METHOD
This
systematic
literature
re
vie
w
w
as
conducted
to
identify
,
e
v
aluate,
and
synthesiz
e
recent
resea
rch
on
computational,
sanad-based
hadith
analysis.
The
process
follo
wed
a
structured
protocol
to
ensure
a
compre-
hensi
v
e
and
unbiased
o
v
ervie
w
of
the
current
state
of
the
eld.
The
search
w
as
performed
on
major
academic
databases,
including
IEEE
Xplore,
A
CM
Digital
Library
,
Scopus,
Google
Scholar
,
and
the
preprint
repository
arXi
v
.
The
search
w
as
conducted
using
a
combination
of
k
e
yw
ords
designed
to
capture
the
rele
v
ant
literature
across
computer
science
and
Islamic
studies.
The
primary
k
e
yw
ords
included:
”hadith
sanad
analysis”,
”hadith
classication”,
”automated
hadi
th
authentication”,
”hadith
narrator
netw
ork”,
”social
netw
ork
analysis
hadith”
[13],
[17],
”sanad
graph”
[15],
[21],
”NLP
hadith”,
”sanad
e
xtraction”,
”narrator
name
disambiguation”,
”hadith
ontology”
[16],
[22],
and
”hadith
dataset”
[12],
[23]-[25].
T
o
ensure
the
inclusion
of
the
latest
adv
ancements,
terms
such
as
”ML”,
”DL”,
and
”BER
T”
[18]
were
combined
with
the
primary
k
e
yw
ords.
Studies
were
included
if
the
y
met
the
follo
wing
criteria:
i)
the
primary
focus
w
as
on
the
sanad
(chain
of
narrators)
of
hadith;
ii)
the
study
applied
computational
methods,
including
b
ut
not
limited
to
ML,
data
mining,
natural
language
processing
(NLP),
or
netw
ork
analysis;
iii)
the
paper
w
as
published
in
English
in
a
peer
-re
vie
wed
journal,
conference
proceeding,
or
as
a
publicly
a
v
ailable
technical
report
or
preprint;
and
i
v)
the
article
w
as
published
between
2012
and
early
2025
to
capture
a
decade
of
adv
ancements
while
prioritizing
recent
w
ork.
P
apers
focusi
ng
e
xclusi
v
ely
on
the
Matn
(te
xt)
wi
thout
sanad
analysis,
purely
theological
or
historical
studies
without
a
computational
component,
and
articles
not
a
v
ailable
in
full-te
xt
were
e
xcluded.
The
initial
search
yielded
numerous
articles,
which
were
then
screened
by
title
and
abstract,
follo
wed
by
a
full-te
xt
re
vie
w
to
determine
nal
eligibility
.
This
process
(Figure
1)
ensured
that
the
included
studies
were
directly
rele
v
ant
to
the
scope
of
this
re
vie
w
.
Computational
methodolo
gies
for
sanad-based
hadith
analysis:
a
r
e
vie
w
(Abdelilah
Mhamedi)
Evaluation Warning : The document was created with Spire.PDF for Python.
842
❒
ISSN:
1693-6930
I
d
e
n
t
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fi
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a
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=
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Figure
1.
PRISMA
lo
w
diagram
for
the
systematic
re
vie
w
on
computational
hadith
studies
This
PRISMA
2020
[26]
o
w
diagram
see
in
Figure
1
systematically
maps
the
process
of
i
dentifying,
screening,
and
including
studies
for
the
systematic
literature
re
vie
w
on
computational
t
ools
and
t
echniques
in
hadith
studies.
It
details
the
number
of
records
identied
from
v
arious
databases,
those
remo
v
ed
before
screen-
ing,
and
the
subsequent
stages
of
record
screening,
full-te
xt
re
vie
w
,
and
ultimate
inclusion
in
the
qualitati
v
e
synthesis.
The
scope
of
the
re
vie
w
w
as
rigorously
dened
by
a
specic
Boolean
search
query
architecture
(as
illustrated
in
the
query
design
Figure
2).
S
e
a
r
c
h
Q
u
e
r
y
A
N
D
O
R
O
R
"network
analysis"
"graph"
N
e
t
w
o
r
k
s
K
e
y
w
o
r
d
s
O
R
"ontology"
"semantic"
S
e
m
a
n
t
i
c
s
K
e
y
w
o
r
d
s
O
R
"dataset"
"corpus"
D
a
t
a
K
e
y
w
o
r
d
s
O
R
"LMM"
"AI"
"ML"
"NLP"
A
I
T
e
c
h
n
o
l
o
g
i
e
s
K
e
y
w
o
r
d
s
C
o
m
p
u
t
e
r
S
c
i
e
n
c
e
K
e
y
w
o
r
d
s
A
N
D
O
R
"analysis"
"classification"
"authentication"
"extraction"
"disambiguation"
T
a
s
k
K
e
y
w
o
r
d
s
O
R
O
R
"Hadith"
"Sanad"
"ISNAD"
S
A
N
A
D
K
e
y
w
o
r
d
s
O
R
"Hadith
narrator"
"narrator"
N
A
R
R
A
T
O
R
K
e
y
w
o
r
d
s
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c
o
p
e
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y
w
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r
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l
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m
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c
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t
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d
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e
s
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e
y
w
o
r
d
s
P
r
i
m
a
r
y
K
e
y
w
o
r
d
s
Figure
2.
Search
query
design
TELK
OMNIKA
T
elecommun
Comput
El
Control,
V
ol.
24,
No.
3,
June
2026:
840–851
Evaluation Warning : The document was created with Spire.PDF for Python.
TELK
OMNIKA
T
elecommun
Comput
El
Control
❒
843
This
architecture
utilized
primary
k
e
yw
ords
connected
by
the
operator
AND
to
ensure
that
the
ident
i-
ed
studies
operated
at
the
critical
intersection
of
tw
o
core
domains:
Islamic
studies
and
computer
science.
The
architecture,
designed
using
the
principle
of
grouping
related
terms
with
OR
and
enforcing
connecti
on
using
AND,
w
as
structured
as
detailed
in
the
T
able
1.
T
able
1.
K
e
yw
ord
cate
gories
and
boolean
connections
Cate
gory
(K
e
yw
ords)
Example
terms
and
purpose
Boolean
connection
Sanad/narrator
k
e
yw
ords
(Islamic
studies
focus)
T
erms
lik
e
”hadith”,
”sanad”,
”hadith
narrator”,
or
”narrator”
were
used
to
ensure
focus
on
the
chain
of
narrators
Connected
by
OR
within
the
group
Computational/AI
technologies
k
e
yw
ords
(computer
science
focus)
T
erms
such
as
”LMM”,
”AI”,
”ML”,
and
”NLP”
captured
the
required
computational
methodology
Connected
by
OR
within
the
group
T
ask
k
e
yw
ords
T
erms
lik
e
”analysis”,
”classication”,
”authentication”,
”e
x-
traction”,
or
”disambiguation”
tar
geted
specic
computational
goals
Connected
by
OR
within
the
group
Structure
k
e
yw
ords
T
erms
lik
e
”graph”,
”netw
orks”,
”ontology”,
”semantic”,
”dataset”,
and
”corpus”
captured
research
related
to
data
struc-
ture
and
formal
kno
wledge
representation
Connected
by
OR
within
the
group
Further
modications
and
adaptations
were
implemented
to
align
with
the
specic
requirements
of
each
search
engine,
thereby
enabling
the
renement
of
search
results
either
narro
wing
or
broadening
their
scope
as
necessary
.
In
this
conte
xt,
eld-specic
constraint
s
are
applied
to
ensure
that
k
e
y
terms
(e.g.,
sanad)
are
restricted
to
semantically
rele
v
ant
metadata
elds
such
as
title
or
abstract,
while
e
xplicitl
y
e
xcluded
from
non-rele
v
ant
elds
such
as
author
,
thus
enhancing
precision
and
reducing
noise
in
retrie
v
al.
3.
RESUL
TS
AND
DISCUSSION
Our
re
vie
w
or
g
anizes
the
ndings
into
four
principal
domains
of
sanad-based
research.
Each
domain
sho
ws
a
clear
methodological
e
v
olution
from
rule-based
systems
to
sophisticated
machine
and
DL
approaches
(Figure
3)
[27]-[57].
This
section
pro
vides
a
detailed
discussion
of
the
ndings
within
each
cate
gory
,
high-
lighting
k
e
y
techniques,
their
performance,
and
pre
v
ailing
challenges,
supplemented
by
summary
tables
for
clarity
.
Figure
3.
T
imeline
of
sanad-based
research,
illustrating
the
methodological
e
v
olution
in
each
domain
from
early
rule-based
systems
to
adv
anced
machine
and
DL
approaches
3.1.
A
utomated
hadith
classication
f
or
athenticity
assessment
Automating
the
classication
of
hadith
into
traditional
cate
gories
such
as
Sahih
(authentic),
Hasan
(good),
Da’if
(weak),
or
Mawdu’
(f
abricated)
is
a
pr
imary
objecti
v
e
of
computational
hadith
studies
[48],
[6].
Computational
methodolo
gies
for
sanad-based
hadith
analysis:
a
r
e
vie
w
(Abdelilah
Mhamedi)
Evaluation Warning : The document was created with Spire.PDF for Python.
844
❒
ISSN:
1693-6930
Early
attempts
in
this
area
relied
on
fuzzy
e
xpert
systems
and
rule-based
models
that
sought
to
codify
the
principles
of
hadith
criticism.
F
or
e
xample,
Ghazizadeh
et
al.
[1]
proposed
a
fuzzy
e
xpert
system
to
determine
hadith
v
alidity
by
modeling
parameters
lik
e
narrator
character
and
sanad
continuity
,
achie
ving
94%
accurac
y
on
a
subset
of
the
Shiite
coll
ection
Al-Ka.
While
inno
v
ati
v
e,
these
systems
were
often
brittle,
collection-specic,
and
dif
cult
to
scale.
The
eld
has
since
shifted
decisi
v
ely
to
w
ards
ML.
Studies
ha
v
e
successfully
emplo
yed
supervised
learning
algorithms
where
features
are
deri
v
ed
from
the
sanad.
Aldhlan
et
al.
[8],
[9],
[11]
presented
a
series
of
papers
using
decision
trees
(DT)
and
Na
¨
ıv
e
Bayes
(NB),
incorporating
a
missing
data
detector
(MDD)
to
handle
incomplete
narrator
information,
which
signicantly
boosted
their
classication
accurac
y
from
50%
to
97%
on
a
dataset
of
999
hadiths.
Other
researchers
le
v
eraged
heuristic-based
systems
that
assigned
weights
to
narrators
based
on
their
rank
in
classical
biographical
dictionaries
lik
e
Ibn
Hajar’
s
T
aqrib
al-T
ahzib
,
reporting
remarkable
accuracies
of
o
v
er
94%
on
lar
ge
samples
from
Sahih
Bukhari
and
Sunan
al-T
irmizi
[13].
Later
w
ork
e
xplored
v
ector
space
models
(VSM)
and
learning
v
ector
quantizati
on
(L
VQ)
to
consider
the
order
of
narrators,
achie
ving
80%
precision
in
distinguishing
between
Sahih
and
f
abricated
hadiths,
though
performance
on
Hasan
and
weak
cate
gories
w
as
lo
wer
[16].
More
recently
,
DL
models,
particularly
those
based
on
the
transformer
architecture
lik
e
BER
T
,
are
being
e
xplored
for
their
abi
lity
to
process
ra
w
te
xt
and
learn
comple
x
representations
without
manual
feature
engineering
[19],
[29].
Abdelaal
et
al.
[23]
used
n-gram
techniques
(trigrams)
and
TF-IDF
weighting
with
classiers
lik
e
linear
SVC,
achie
ving
up
to
93.69%
accurac
y
.
Sentiment
analysis
has
also
been
applied,
where
narrator
names
in
the
sanad
are
treated
as
tok
ens
to
predict
authenticity
,
reaching
80%
accurac
y
with
a
linear
SVC
model
[21].
A
2022
study
e
xploring
DL
for
binary
hadith
classication
(aut
hentic
vs.
rejected)
found
that
an
AraBER
T
model
achie
v
ed
an
accurac
y
of
91.56%
[19].
These
results
underscore
the
po
wer
of
con-
te
xtual
embeddings
for
capturing
the
nuanced
information
within
narrator
chains.
The
progression
of
these
methodologies
is
summarized
in
T
able
2.
T
able
2.
Studies
concentrated
on
the
cate
gorization
of
hadiths
Ref.
Approach
Preprocessing
Classes
Language
Data
source
Metric
Result
[1]
Fuzzy
system
-
V
alid
and
not
v
alid
-
El-Ka
Accurac
y
94%
[8]-[11]
DT
,
NB
-
V
alid
and
not
v
alid
Arabic
999
hadiths
Accurac
y
97%
[12]
SaaS,
SO
A
-
24
classes
-
-
-
-
[13]
HR
Name
normaliza-
tion
Sahih,
Hasan,
weak
Arabic
Bukhari,
T
ir
-
midhi
Accurac
y
99%
[14],
[15]
ANLP
,
ANN,
SVM,
DT
,
BC
-
Sound,
weak
Arabic
-
-
-
[16]
SVM,
L
VQ
Remo
v
e
Matn,
standardize
names
Sahih,
Hasan,
weak,
f
abricated
Arabic
160
hadiths
Precision
80%
[17],
[23]
DT
,
NB,
linear
SVC,
SGD,
LR
Normalization
and
tok
enization
Sahih,
Hasan,
weak,
f
abricated
Arabic
Bukhari
and
Muslim
Accurac
y
up
to
93.75%
[45]
Doc2V
ec
Stop
w
ords
and
lemmatization
Hadith
similari-
ties
Arabic
9
books
Accurac
y
80%
[21]
Sentiment
analysis
T
ok
enization,
v
ectorization
Sahih/Hasan,
weak
English
Bukhari,
Muslim,
T
irmidhi
Accurac
y
86%
[57]
LR,
SVM,
RF
,
AraBER
T
REGEX-based
preprocessing
Genuine,
f
ak
e
Arabic
Al-Bukhari,
and
f
ak
e
hadiths
F1-score
99.94%
[56]
ArabicBER
T
,
NB,
DL,
CNN-LSTM
-
Sahih,
Hasan,
and
Da’if
and
accepted/rejected
English
full-te
xt,
sanad-only
F1-score
94.90%
[54]
AraBER
T
,
HistGra-
dientBoostingClas-
sier
TF-IDF
Three
questions
with
0,1
or
2
output
Arabic
503
para-
graphs
Accurac
y
93.16%,
96.55%
[55]
Sanad
authentica-
tion
fuzzy
e
xpert
system
-
Sahih,
Mudallas,
Hasan,
Matruk,
.
.
.
Arabic
5,910
chain
of
narrators
Accurac
y
72.2%
TELK
OMNIKA
T
elecommun
Comput
El
Control,
V
ol.
24,
No.
3,
June
2026:
840–851
Evaluation Warning : The document was created with Spire.PDF for Python.
TELK
OMNIKA
T
elecommun
Comput
El
Control
❒
845
3.2.
Building
and
analysing
hadith
narrator
netw
orks
V
ie
wing
the
entire
corpus
of
hadith
transmission
as
a
lar
ge-scale
social
netw
ork
has
opened
ne
w
a
v
enues
for
analysis.
In
this
paradigm,
narrators
are
represented
as
nodes
and
the
transmission
of
a
hadith
from
one
narrator
to
another
is
represented
as
a
directed
edge.
This
frame
w
ork
allo
ws
for
the
application
of
social
netw
ork
analysis
(SN
A)
and
graph
theory
to
in
v
estig
ate
the
structural
properties
of
hadith
transmission
netw
orks
[13],
[46].
Early
conceptual
w
ork
proposed
modeling
narrator
chains
using
graph-theoretic
models
lik
e
di
rected
ac
yclic
graphs
(D
A
Gs)
to
represent
the
o
w
of
information
[18].
Subsequent
research
focused
on
creating
tools
to
automatically
parse
and
visualize
these
chains.
Azmi
and
Badia
de
v
eloped
the
”e-Narrator”
and
”iT
ree”
systems,
which
used
parsing
techniques
and
domain-specic
grammars
(EBNF)
to
generate
transmission
trees
from
ra
w
hadith
te
xt,
achie
ving
an
86.7%
success
rate
on
a
dataset
of
90
hadiths
[27],
[58],
[24].
Other
prototypes,
lik
e
the
chain
of
hadith
narrators
visualizer
(CHN),
pro
vided
graphical
interf
aces
for
students
to
e
xplore
narrator
connections
[19].
More
adv
anced
analyses
ha
v
e
applied
formal
SN
A
metrics
to
these
narrator
graphs.
Studies
on
Sahih
Bukhari
and
Sahih
Muslim
ha
v
e
used
centrality
measures
(de
gree,
betweenness,
P
ageRank)
to
identify
the
most
inuential
narrators
in
the
netw
ork,
such
as
Ab
u
Hurayra,
Anas
bin
Malik,
and
Az-Zuhri,
who
acted
as
major
hubs
in
the
propag
ation
of
kno
wledge
[22],
[10],
[49].
These
ndings
quantitati
v
ely
conrm
kno
wledge
pre
viously
established
by
classical
schol
ars.
Other
researchers
ha
v
e
used
al
gorithms
lik
e
SP
ADE
to
disco
v
er
frequent
sequential
patterns
in
narrator
chains,
re
v
ealing
dominant
teacher
-student
relationships
and
common
transmission
pathw
ays
[48].
A
signicant
rec
ent
trend
is
the
use
of
graph
embedding
techniques.
Mghari
et
al.
[4]
introduced
Narrator2V
ec,
a
method
that
learns
v
ector
representations
(embeddings)
of
narrators
based
on
their
position
in
the
netw
ork.
These
embeddings
can
be
used
for
tasks
such
as
predicting
missing
links
in
a
sanad
(link
prediction),
clustering
narrators
by
generation
or
scholarly
af
liation,
and
identifying
narrator
similarity
.
When
tested
on
the
all
Hadith
Corpus,
Narrator2V
ec
achie
v
ed
94%
accurac
y
in
top-10
narrator
prediction
tasks.
These
graph-based
approaches
pro
vide
in
v
aluable
macroscopic
vie
ws
of
the
hadith
transmission
landscape,
highlighting
its
scale-free
nature
and
identifying
k
e
y
communities
and
authorities
within
it.
A
summary
of
these
analytical
approaches
is
sho
wn
in
T
able
3.
T
able
3.
Exploring
analytical
approaches
for
hadith
using
sanads
Ref.
Approach
Preprocessing
Classes
Language
Data
source
Metric
Result
[18]
D
A
G
-
Sanad
representa-
tion
Arabic
-
-
-
[24]-[27]
EBNF
P
arse
hadith
content
Graph,
visualiza-
tion
Arabic
Bukhari
Accurac
y
86.7%
[19]
Netw
ork
graph
-
Graph,
visualiza-
tion
Malay
Na
w
a
wi’
s
40
hadiths
-
-
[22]
SN
A
Extract
narrators,
remo
v
e
du-
plicates
Narrati
v
e
net-
w
ork
analysis
Arabic
Bukhari
5
chapters
Centrality
-
[25],
[35]
Netw
ork
graph
Matn
and
stop
w
ords
remo
v
al
Graph
Malay
18/30
Ha-
diths
from
9-
books/Bukhari
Accurac
y
60%
[10]
SN
A
-
Narrati
v
e
net-
w
ork
Arabic
Bukhari
Centrality
-
[48]
SP
ADE
T
ransform,
clean,
format
Sanad
analysis
Indonesian
Bukhari
-
-
[49]
SN
A
-
Narrati
v
e
net-
w
ork
analysis
English
Muslim
SN
A
measures
-
[4]
W
ord
embed-
dings
-
Narrators
analy-
sis
Arabic
All
hadith
corpus
T
op-k
accuracies
68-94%
3.3.
Identifying
and
extracting
k
ey
components
fr
om
hadith
text
The
automated
e
xtraction
of
k
e
y
information
from
hadith
te
xt
particularly
separating
the
sanad
fr
om
the
Matn
and
identifying
indi
vidual
narrator
names
within
the
sanad
is
a
fundamental
task
f
o
r
b
uilding
struc-
tured
datasets.
This
is
a
challenging
NLP
problem
due
to
the
linguistic
characteristics
of
classical
Arabic,
the
Computational
methodolo
gies
for
sanad-based
hadith
analysis:
a
r
e
vie
w
(Abdelilah
Mhamedi)
Evaluation Warning : The document was created with Spire.PDF for Python.
846
❒
ISSN:
1693-6930
lack
of
standard
punctuation,
and
the
high
v
ariability
in
ho
w
narrators’
names
are
cited
[50].
Early
methods
used
rule-based
approaches
and
nite
state
transducers
(FST).
Harrag
et
al
.
[2],
[3]
de
v
eloped
an
FST
-based
entity
e
xtractor
for
Sahih
Al-Bukhari,
b
ut
it
struggled
with
the
sanad
entity
itself,
achie
ving
a
lo
w
F1-score
of
33%.
Unsupervised
tools
lik
e
the
SALAH
Project
used
re
gular
e
xpressions
to
se
gment
hadith
te
xts,
achie
ving
a
high
ef
fecti
v
eness
rate
of
97.7%
b
ut
with
limitations
on
handling
comple
x
chains
[5].
Other
studies
emplo
yed
comple
x
graph
transformations
and
morphological
analysis
to
e
xtract
nar
-
rator
relationships,
reporting
high
precision
and
recall
abo
v
e
97%
for
se
gmentation
tasks
[6],
[7].
The
eld
has
seen
signicant
i
mpro
v
em
ent
with
the
adoption
of
ML-based
named
entity
recognition
(NER).
Siddiqui
et
al.
[29]
trained
classiers
lik
e
Na
¨
ıv
e
Bayes,
DT
,
and
K-nearest
neighbors
(k-NN)
on
an
annotated
corpus
to
e
xtract
narrator
names,
achie
ving
90%
precision.
Najeeb
[46],
[50]
introduced
approaches
using
genetic
algorithms
(GA)
and
hidden
Mark
o
v
models
(HMM)
for
sanad
processing,
reaching
accuracies
of
81.44%
and
86%
respecti
v
ely
.
N-gram
models
combined
with
rule-based
ltering
ha
v
e
also
been
used
to
e
xtract
Arabic
Person
Names,
yielding
an
F-measure
of
70.76%
[20].
The
most
signicant
recent
adv
ancements
ha
v
e
come
from
transformer
-based
models
[29].
These
models
le
v
erage
conte
xtual
embeddings
to
achie
v
e
state-of-the-art
performance.
F
or
instance,
a
study
using
a
semi-supervised
BER
T
model
with
a
feed-forw
ard
neural
netw
ork
for
NER
on
Indonesian
hadith
te
xts
reported
an
F1-score
of
99.27%
on
a
dataset
from
Bukhari
[51].
Another
line
of
research
focused
on
name
disam-
biguation
using
w
ord
sense
disambiguation
(WSD)
techniques
combined
with
a
k-NN
classier
,
reporting
an
F1-score
of
99%
on
a
dataset
from
Sahih
Bukhari,
demonstrating
a
po
werful
method
to
resolv
e
narrator
ambi-
guity
[59].
These
DL
techniques
are
pro
ving
indispensable
for
accurately
parsing
and
structuring
hadith
te
xts
at
scale,
a
prerequisite
for
all
other
higher
-le
v
el
analyses.
T
able
4
pro
vides
an
o
v
ervie
w
of
v
arious
studies
in
this
cate
gory
.
T
able
4.
Studies
e
xploring
approaches
and
techniques
for
named
entity
e
xtraction
Ref.
Approach
Preprocessing
Extracted
entities
Language
Data
source
Metric
Result
[2],
[3]
FST
-based
-
Sanad
e
xtraction
Arabic
Bukhari
F1-score
33%
[5]
RE
-
Sanad
Arabic/English
Bukhari
Accurac
y
97.7%
[6],
[7]
AMF
,
FSM,
GT
-
Sanad,
narrators
Arabic
Ibn
Hanbal
F-score
98%
[29]
NER,
NB,
k-NN,
DT
Normalization,
stemming
Sanad
e
xtraction
Arabic
Bukhari,
Musnad
Ahmed
Precision
90%
[46]
GA
Sanad/Matn
manu-
ally
di
vided
Sanad
e
xtraction
Arabic
Muslim
Accurac
y
81.44%
[50]
HMM,
Gazetteer
Sanad/Matn
manu-
ally
di
vided
Sanad
e
xtraction
Arabic
Muslim
Accurac
y
86%
[20]
N-gram
-
Narrators
e
xtrac-
tion
Arabic
6
books
F-measure
65.11%
[31]
POS
tags,
rule-
based
Punctuation
re-
mo
v
al
Sanad,
narrators
Malay
150/1000
from
Bukhari
Accurac
y
95.3%
[36]
Rule-based,
Sta-
tistical
T
ok
enization,
Stemming
Narrators
Arabic
Bukhari
(prayer)
F1-score
76%
[37]
CRF
,
FST
-
-
Urdu
Bukhari
F1-score
92.41%
[40]
NER,
SVM
Symbol
remo
v
al,
tok
enization
Narrators
e
xtrac-
tion
Indonesian
200
from
9
books
F1-score
90%
[41],
[47]
Rule-based,
NB
Diacritics/punctuation
remo
v
al
Sanad
e
xtraction
Arabic/English
6
books
Accurac
y
92.5%
[42]
HMM
Punctuation
re-
mo
v
al,
T
ok
eniza-
tion
Names
e
xtraction
Indonesian
9
books
F1-score
86%
[51]
NER,
BER
T
-
based
T
ok
enization
Narrators
e
xtrac-
tion
Indonesian
102
from
Bukhari
F1-score
99.63%
3.4.
Construction
and
de
v
elopment
of
sanad
datasets
and
ontologies
The
foundation
for
all
computational
hadith
research
is
the
a
v
ailability
of
high-quality
,
structured
digital
resources.
A
signicant
area
of
w
ork,
therefore,
in
v
olv
es
the
construction
of
comprehensi
v
e
datasets
and
formal
ontologies
to
support
reproducible
research
and
adv
anced
applications.
Early
w
ork
focused
on
creating
structured
databases
from
ra
w
te
xt.
Bimba
et
al.
[34]
de
v
eloped
a
TELK
OMNIKA
T
elecommun
Comput
El
Control,
V
ol.
24,
No.
3,
June
2026:
840–851
Evaluation Warning : The document was created with Spire.PDF for Python.
TELK
OMNIKA
T
elecommun
Comput
El
Control
❒
847
web-based
tool
with
a
relational
MySQL
database
to
compi
le
authentic
hadith
in
Malay
,
linking
te
xt
to
reporter
information.
Other
researchers
focused
on
b
uilding
le
xicons
using
formalisms
lik
e
head-dri
v
en
phrase
structure
grammar
(HPSG)
and
creating
XML-based
databases
of
narrators
and
their
biographical
details,
often
dra
wing
from
classical
sources
lik
e
Ibn
Hajar’
s
w
ork
[32],
[33],
[60].
The
te
xt
encoding
initiati
v
e
(TEI)
standard
w
as
also
adopted
to
normalize
and
encode
hadith
te
xts,
with
studies
de
v
eloping
trigger
-w
ord
dictionaries
to
se
gment
Isnad
from
Matn,
achie
ving
a
high
F-measure
of
96%
[38],
[43],
[61].
A
major
contrib
ution
to
the
eld
has
been
the
de
v
elopment
of
lar
ge-scale,
public
datasets.
Mahmood
et
al.
[39]
created
a
multilingual
repository
of
hadith
content
e
xtracted
from
online
sources
using
re
gular
e
xpressions,
achie
ving
100%
accurac
y
for
some
books.
Other
projects
ha
v
e
focused
on
creating
specialized
corpora,
such
as
a
non-authentic
hadith
(N
AH)
corpus
to
train
models
to
detect
f
abricated
narrations
[44].
Most
signicantly
,
recent
ef
forts
ha
v
e
produced
lar
ge-scale
datasets
for
narrator
disambiguati
o
n,
such
as
the
AR-sanad
280
K
dataset,
which
contains
279,625
articial
sanads.
Experiments
using
this
dataset
with
an
AraBER
T
model
achie
v
ed
a
92.9%
micro
F1
score,
demonstrating
the
v
alue
of
lar
ge-scale
synthetic
data
for
training
rob
ust
models
[52].
Alongside
datasets,
there
is
gro
wing
interest
in
de
v
eloping
formal
ontologies
for
hadith.
Ontol
ogies
pro
vide
a
machine-readable
representation
of
kno
wledge,
dening
concepts
(e.g.,
narrator
,
hadith)
and
their
relationships
(e.g.,
‘narr
ates‘
).
These
semantic
models
f
acilitate
adv
anced
querying
and
logical
inference.
Dalloul
and
Baraka
created
an
ontology-based
Isnad
judgment
system
that
could
automatically
v
erify
chain
continuity
based
on
narrator
relationships,
achi
e
ving
81%
accurac
y
[30],
[28].
These
resources
are
foundational
for
b
uilding
the
ne
xt
generation
of
intel
ligent
hadith
analysis
tools.
An
o
v
ervie
w
of
these
construction
ef
forts
is
pro
vided
in
T
able
5.
T
able
5.
Studies
on
the
construction
and
de
v
elopment
of
hadith-specic
datasets
and
ontologies
Ref.
Approach
Data
type
Language
Data
source
Met
ric
Result
[28],
[30]
Sanad
ontology
Arabic
6
Books
Accurac
y
81%
[32],
[33]
XML,
HPSG,
Multi-agent
XML,
dataset,
Is-
nad
tree
Arabic
Bukhari,
Muslim
-
-
[34]
MySQL
dataset
Malay
-
-
-
[38],
[43]
TEI
predened
XML
tags
Dataset
Arabic
Bukhari
F-measure
85-96%
[39]
Re
gular
e
xpres-
sion
Dataset,
XML,
CSV
Multilingual
Muslim
and
Bukhari
F-measure
100%
[44]
-
Dataset,
sanad
Arabic
6
books
-
-
[52]
BER
T
-based
(AraBER
T)
Dataset,
disam-
biguation
Arabic
6
books
F1-score
92.9%
[53]
RDF
,
kno
wledge
graph,
link
ed
open
data
SemanticHadith
ontology
,
kno
wl-
edge
graph
Arabic,
Urdu,
English
Six
hadith
collections
-
-
4.
CONCLUSION
This
comprehensi
v
e
re
vie
w
has
charted
the
signicant
progress
in
the
application
of
computat
ional
methods
to
sanad-based
hadith
analysis.
The
eld
is
rapidly
maturing,
mo
ving
from
foundational
rule-based
systems
to
sophisticated
DL
and
netw
ork
science
methodologies.
The
analysis
of
automated
classication
tech-
niques
sho
ws
a
clear
performance
adv
antage
for
ML
o
v
er
static
rules,
with
recent
transformer
-based
models
lik
e
BER
T
setting
ne
w
benchmarks
for
authenticity
assess
ment.
The
e
xploration
of
narrator
netw
orks
through
SN
A
has
pro
vided
quantitati
v
e
v
alidation
of
classical
hadith
scholarship
and
of
fers
po
werful
tools
for
visualiz-
ing
and
understanding
the
macro-structure
of
kno
wledge
transmission
in
Islam.
Concurrently
,
adv
ancements
in
NLP
ha
v
e
been
critical
in
automating
the
foundational
tasks
of
sanad
se
gmentation
and
narrator
entity
recog-
nition,
making
lar
ge-scale
analysis
feasible.
Finally
,
the
de
v
elopment
of
lar
ge,
publicly
a
v
ailable
datasets
and
formal
ontologies
is
pro
viding
the
essential
infrastructure
to
fuel
further
research
and
ensure
reproducibility
.
These
adv
ancements
collecti
v
ely
mo
v
e
the
body
of
scientic
kno
wledge
forw
ard
by
demonstrating
the
immense
potential
of
interdisciplinary
collaboration
between
computer
science
and
Islamic
studies.
Despite
this
progress,
challenges
remain.
Man
y
de
v
eloped
datasets
are
still
limited
in
scope,
and
t
he
models
trained
on
them
may
not
generalize
well
across
dif
ferent
hadith
collections.
The
problem
of
narrator
name
disambiguation
remains
a
signicant
hurdle,
though
r
ecent
graph-based
and
BER
T
-po
wered
approaches
Computational
methodolo
gies
for
sanad-based
hadith
analysis:
a
r
e
vie
w
(Abdelilah
Mhamedi)
Evaluation Warning : The document was created with Spire.PDF for Python.
848
❒
ISSN:
1693-6930
sho
w
great
promise.
The
hea
vy
reliance
on
re
gular
e
xpressions
in
some
data
e
xtraction
tas
k
s
can
be
brittle,
and
the
creation
of
rob
ust,
adaptable
NLP
pipelines
is
an
ongoing
area
of
research.
Looking
forw
ard,
future
w
ork
should
focus
on
creating
lar
ger
,
more
di
v
erse,
and
standardized
benchmark
datasets
that
co
v
er
a
wider
range
of
hadith
literature,
including
less-canonical
w
orks.
There
is
great
promise
in
e
xploring
more
adv
anced
graph
neural
netw
ork
(GNN)
architectures
for
narrator
netw
ork
analysis
and
le
v
eraging
multimodal
models
that
can
analyze
both
sanad
and
Matn
concurrently
to
create
a
more
holistic
authe
n
t
icity
assessment.
The
continued
partnership
between
computer
scientists
and
traditional
hadith
scholars
is
essenti
al
to
ensure
that
these
technological
adv
ancements
are
de
v
eloped
responsi
bly
,
rigorously
,
and
in
a
w
ay
that
genuinely
supports
and
enhances
our
understanding
of
the
rich
heritage
of
Islamic
traditions.
FUNDING
INFORMA
TION
Authors
state
no
funding
in
v
olv
ed.
A
UTHOR
CONTRIB
UTIONS
ST
A
TEMENT
This
journal
uses
the
C
o
nt
rib
utor
Roles
T
axonomy
(CRediT)
to
recognize
indi
vidual
author
contrib
u-
tions,
reduce
authorship
disputes,
and
f
acilitate
collaboration.
Name
of
A
uthor
C
M
So
V
a
F
o
I
R
D
O
E
V
i
Su
P
Fu
Abdelilah
Mhamedi
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
Mohammed
Mghari
✓
✓
✓
✓
✓
✓
Abdelaaziz
El
Hibaoui
✓
✓
✓
✓
✓
✓
✓
C
:
C
onceptualization
I
:
I
n
v
estig
ation
V
i
:
V
i
sualization
M
:
M
ethodology
R
:
R
esources
Su
:
Su
pervision
So
:
So
ftw
are
D
:
D
ata
Curation
P
:
P
roject
Administration
V
a
:
V
a
lidation
O
:
Writing
-
O
riginal
Draft
Fu
:
Fu
nding
Acquisition
F
o
:
F
o
rmal
Analysis
E
:
Writing
-
Re
vie
w
&
E
diting
CONFLICT
OF
INTEREST
ST
A
TEMENT
Authors
state
no
conict
of
interest.
D
A
T
A
A
V
AILABILITY
Data
a
v
ailability
is
not
applicable
to
this
paper
as
no
ne
w
data
were
created
or
analyzed
in
this
study
.
REFERENCES
[1]
M
.
Ghazizadeh,
M.
H.
Zahedi,
M.
Kahani,
and
B.
Minaei
Bidgoli,
“Fuzzy
e
xpert
system
in
determining
hadith
v
alidity
,
”
in
Advances
in
Computer
and
Information
Sciences
and
Engineering
,
T
.
Sobh,
Ed.,
Springer
Netherlands,
2008,
pp.
354–359,
doi:
10.1007/978-
1-4020-8741-7
64.
[2]
F
.
Harrag,
E.
El-Qa
w
asmeh,
and
A.
M.
Salman
Al-Salman,
“Extracting
named
entities
from
prophetic
narration
te
xts
(Hadith),
”
in
Communications
in
Computer
and
Information
Science
,
v
ol.
180
CCIS,
no.
P
AR
T
2,
J.
M.
Zain,
W
.
M.
B.
W
an
Mohd,
and
E.
El-Qa
w
asmeh,
Eds.,
Springer
Berlin
Heidelber
g,
2011,
pp.
289–297,
doi:
10.1007/978-3-642-22191-0
26.
[3]
F
.
Harrag,
“T
e
xt
mining
approach
for
kno
wledge
e
xtraction
in
Sah
ˆ
ıh
Al-Bukhari,
”
Computer
s
in
Human
Behavior
,
v
ol.
30,
pp.
558–566,
2014,
doi:
10.1016/j.chb
.2013.06.035.
[4]
M
.
Mghari,
O.
Bouras,
and
A.
El
Hibaoui,
“Narrator2V
ec:
An
Ef
cient
Narrator
Representation
in
Hadith
Literature
Using
W
ord
Embedding,
”
Ar
abian
J
ournal
for
Science
and
Engineering
,
v
ol.
49,
no.
3,
pp.
4479–4494,
2024,
doi:
10.1007/s13369-023-08224-7.
[5]
M
.
Boella,
F
.
R.
Romani,
A.
Al-Raies,
C.
Solimando,
and
G.
Lancioni,
“The
SALAH
project:
Se
gmentation
and
linguistic
analysis
of
had
¯
ıt
Arabic
te
xts,
”
in
Lectur
e
Notes
in
Computer
Science
(including
subseries
Lectur
e
Notes
in
Articial
Intellig
ence
and
Lectur
e
Notes
in
Bioinformatics)
,
v
ol.
7097
LNCS,
M.
V
.
M.
Salem,
K.
Shaalan,
F
.
Oroumchian,
A.
Shak
ery
,
and
H.
Khelalf
a,
Eds.,
Springer
Berlin
Heidelber
g,
2011,
pp.
538–549,
doi:
10.1007/978-3-642-25631-8
49.
[6]
J.
Makhlouta,
F
.
Zarak
et,
and
H.
Hark
ous,
“
Arabic
enti
ty
graph
e
xtracti
on
using
morphology
,
nite
state
machines,
and
graph
transformations,
”
in
Lectur
e
Notes
in
Computer
Science
(including
subseries
Lectur
e
Notes
in
Arti
cial
Intellig
ence
and
Lectur
e
Notes
in
Bioinformatics
),
v
ol.
7181
LNCS,
no.
P
AR
T
1,
A.
Gelb
ukh,
Ed.,
Springer
Berlin
Heidelber
g,
2012,
pp.
297–310,
doi:
10.1007/978-3-642-28604-9
25.
TELK
OMNIKA
T
elecommun
Comput
El
Control,
V
ol.
24,
No.
3,
June
2026:
840–851
Evaluation Warning : The document was created with Spire.PDF for Python.
TELK
OMNIKA
T
elecommun
Comput
El
Control
❒
849
[7]
F
.
Zarak
et
and
J.
Makhlouta,
“
Arabic
cross-document
NLP
for
the
hadith
and
biograph
y
literature,
”
Pr
oceedings
of
the
25th
Inter
-
national
Florida
Articial
Intellig
ence
Resear
c
h
Society
Confer
ence
,
FLAIRS-25,
pp.
256–261,
2012.
[8]
K.
A.
Aldhlan,
A.
M.
Zeki,
and
A.
M.
Zeki,
“Datamining
and
Islamic
kno
wledge
e
xtraction:
Alhadith
as
a
kno
wledge
resource,
”
in
Pr
oceeding
of
the
3r
d
International
Confer
ence
on
Information
and
Communication
T
ec
hnolo
gy
for
the
Moslem
W
orld:
ICT
Connecting
Cultur
es,
ICT4M
2010,
IEEE
,
2010,
p.
H-21-H-25,
doi:
10.1109/ICT4M.2010.5971934.
[9]
K.
Aldhaln,
A.
Zeki,
A.
Zeki,
and
H.
Alreshidi,
“Impro
ving
kno
wledge
e
xtraction
of
Hadith
classier
using
decision
tree
algorithm,
”
in
Pr
oceedings
-
2012
International
Confer
ence
on
Information
Retrie
val
and
Knowledg
e
Mana
g
ement
,
CAMP’12,
IEEE,
2012,
pp.
148–152,
doi:
10.1109/InfRKM.2012.6205024.
[10]
T
.
Alam
and
J.
Schneider
,
“Social
Netw
ork
Analysis
of
Hadith
Narrators
from
Sahih
Bukhari
,
”
Pr
oceedings
of
2020
7th
IEEE
International
Confer
ence
on
Behav
iour
al
and
Social
Computing
,
BESC
2020,
v
ol.
abs/2102.02009,
2020,
doi:
10.1109/BESC51023.2020.9348299.
[11]
K.
A.
Aldhlan,
A.
M.
Zeki
,
A.
M.
Zeki,
and
H.
A.
Alreshidi,
“No
v
el
mechanism
to
impro
v
e
hadith
classier
performance,
”
in
Pr
oceedings
-
2012
International
Confer
ence
on
Advanced
Computer
Science
Applications
and
T
ec
hnolo
gies
,
A
CSA
T
2012,
IEEE,
2013,
pp.
512–517,
doi:
10.1109/A
CSA
T
.2012.93.
[12]
K.
Bilal
and
S.
Mohsin,
“Muhadith:
A
cloud
based
distrib
uted
e
xpert
system
for
classication
of
Ahadith,
”
in
Pr
oceedings
-
10th
International
Confer
ence
on
F
r
ontier
s
of
Information
T
ec
hnolo
gy
,
FIT
2012,
IEEE,
2012,
pp.
73–78,
doi:
10.1109/FIT
.2012.22.
[13]
A.
M.
Azmi,
A.
M.
Alof
aidly
,
”A
no
v
el
method
to
automatically
pass
hukm
on
Hadith,
”
5th
International
Confer
ence
on
Ar
abic
Langua
g
e
Pr
ocessing
(CIT
ALA
’14)
,
2014.
[14]
M.
M.
Najeeb,
“T
o
w
ards
Inno
v
ati
v
e
System
for
Hadith
Isnad
Processing,
”
International
J
ournal
of
Computer
T
r
ends
and
T
ec
hnol-
o
gy
,
v
ol.
18,
no.
6,
pp.
257–259,
2014,
doi:
10.14445/22312803/ijctt-v18p154.
[15]
M.
M.
A.
Najeeb,
“T
o
w
ards
a
Deep
Leaning-based
Approach
for
Hadith
Classication,
”
Eur
opean
J
ournal
of
Engineering
and
T
ec
hnolo
gy
Resear
c
h
,
v
ol.
6,
no.
3,
pp.
9–15,
2021,
doi:
10.24018/ejeng.2021.6.3.2378.
[16]
M.
Ghanem,
A.
Mouloudi,
and
M.
Mourchid,
“Classication
of
Hadiths
using
L
VQ
based
on
VSM
Considering
W
ords
Order
,
”
International
J
ournal
of
Computer
Applications
,
v
ol.
148,
no.
4,
pp.
25–28,
2016,
doi:
10.5120/ijca2016911077.
[17]
H.
M.
Abdelaal
and
H.
A.
Y
ouness,
“Hadith
Classication
using
Machine
Learning
T
echniques
According
to
its
Reliability
,
”
Roma-
nian
J
ournal
of
Information
Science
and
T
ec
hnolo
gy
,
v
ol.
22,
no.
3–4,
pp.
259–271,
2019.
[18]
U.
Relational
and
S.
I.
Hyder
,
“T
o
w
ards
a
Databas
e
Oriented
Hadith
Research
Using
Relational,
Algorithmic
and
Data-W
arehousing
T
echniques,
”
The
Islamic
Cultur
e
,
Quarterly
J
ournal
of
Shaikh
Zayed
Islamic
Center
for
Islamic
and
Ar
abic
Studies
,
v
ol.
19,
no.
March,
p.
14,
2008.
[19]
Z.
Shukur
,
N.
F
abil,
J.
Salim,
and
S.
A.
Noah,
“V
isualization
of
the
hadith
chain
of
narrators,
”
in
Lectur
e
Notes
in
C
omputer
Science
(including
subseries
Lectur
e
Notes
in
Arti
cial
Intellig
ence
and
Lectur
e
Notes
in
Bioinformatics)
,
H.
B.
Zaman,
P
.
Robinson,
M.
Petrou,
P
.
Oli
vier
,
T
.
K.
Shih,
S.
V
elastin,
and
I.
Nystr
¨
om,
Eds.,
Springer
,
2011,
pp.
340–347,
doi:
10.1007/978-3-642-25200-6
32.
[20]
M.
Alha
w
arat,
“
A
domain-based
approach
to
e
xtract
Arabic
person
names
using
N-grams
and
si
mple
rules,
”
Asian
J
ournal
of
Information
T
ec
hnolo
gy
,
v
ol.
14,
no.
8,
pp.
287–293,
2015,
doi:
10.3923/ajit.2015.287.293.
[21]
F
.
Haque,
A.
H.
Orth
y
,
and
S.
Siddique,
“Hadith
Authenticity
Prediction
using
Sentiment
Analysis
and
Machine
Learning,
”
in
14th
IEEE
International
Confer
ence
on
Application
of
Information
and
Communication
T
ec
hnolo
gies,
AICT
2020
-
Pr
oceedings,
Institute
of
Electrical
and
Electr
onics
Engineer
s
Inc
.,
2020,
pp.
1–6,
doi:
10.1109/AICT50176.2020.9368569.
[22]
M.
A.
Ahmad,
“T
o
w
ards
the
Analysis
of
Narrati
v
e
Netw
orks,
”
2013.
Acce
ssed:
Oct.
25,
2025.
[Online].
A
v
ailable:
http://www
.aurumahmad.com/assets/pdf/AhmadNarrati
v
e.pdf
[23]
H.
M.
Abdelaal,
A.
M.
Ahmed,
W
.
Ghribi,
and
H.
A.
Y
.
Alansary
,
“Kno
wledge
Disco
v
ery
in
the
Hadith
According
to
the
Relia-
bility
and
Memory
of
the
Reporters
Using
Machine
Learning
T
echniques,
”
IEEE
Acces
s,
v
ol.
7,
pp.
157741–157755,
2019,
doi:
10.1109/A
CCESS.2019.2944118.
[24]
A.
Azmi
and
N.
AlBadia,
“Mining
and
visualizing
the
narration
tree
of
hadiths
(Prophetic
traditions),
”
Cr
oss-Disciplinary
Advances
in
Applied
Natur
al
Langua
g
e
Pr
ocessing:
Issues
and
Appr
oac
hes
,
pp.
239–257,
2011,
doi:
10.4018/978-1-61350-447-5.ch016.
[25]
N.
Alias,
N.
A.
Rahman,
N.
K.
Ismail,
Z.
M.
Nor
,
and
M.
N.
Alias,
Searching
Algorithm
of
Authentic
Chain
of
Narrators’
in
Shahih
Bukhari
Book
,
no.
May
.
2016.
[26]
M.
J.
P
age
et
al.
,
“The
PRISMA
2020
statement:
An
updated
guideline
for
reporting
systematic
re
vie
ws,
”
BMJ
,
v
ol.
372,
p.
n71,
2021,
doi:
10.1136/bmj.n71.
[27]
A.
M.
Azmi
and
N.
B
in
Badia,
“e-Narrator
-
an
application
for
creating
an
ontology
of
Hadiths
narration
tree
semantically
and
graphically
,
”
Ar
abian
J
ournal
for
Science
and
Engineering
,
v
ol.
35,
no.
2
C,
pp.
51–68,
2010.
[28]
Y
.
M.
Dalloul,
“
An
Ontology-Based
Approach
to
Support
the
Process
of
Judging
Hadith
Isnad,
”
in
2012
International
Confer
ence
on
Advanced
Computer
Science
Applications
and
T
ec
hnolo
gies
,
2013,
pp.
1–108.
[29]
M.
A.
Siddiqui,
M.
E.
Saleh,
and
A.
A.
Bag
ais,
“Extraction
and
V
isualization
of
the
Chain
of
Narrators
from
Hadiths
using
Named
Entity
Recognition
and
Classication,
”
International
J
ournal
of
Computational
Linguistics
Resear
c
h
,
v
ol.
5,
no.
1,
pp.
14–25,
2014.
[30]
R.
S.
Baraka
and
Y
.
M.
Dalloul,
“Building
Hadith
Ontology
to
Support
the
Authenticity
of
Isnad,
”
International
J
ournal
on
Islamic
Applications
in
Computer
Science
And
T
ec
hnolo
gy
,
v
ol.
2,
no.
1,
pp.
25–39,
2014.
[31]
N.
Abd
Rahman,
N.
Alias
,
N.
K.
Ismail,
Z.
Bin
M.
Nor
,
and
M.
N.
B.
Ali
as,
“
An
identication
of
authentic
narrator’
s
name
features
in
Malay
hadith
te
xts,
”
in
ICOS
2015
-
2015
IEEE
Confer
ence
on
Open
Systems
,
IEEE,
2016,
pp.
79–84,
doi:
10.1109/ICOS.2015.7377282.
[32]
M.
Naj
eeb,
A.
Abdelkader
,
M.
Al-Zghoul,
and
A.
Osman,
“
A
Le
xicon
for
Hadit
h
Science
Based
on
a
Corpus,
”
International
J
ournal
of
Computer
Science
and
Information
T
ec
hnolo
gies
,
v
ol.
6,
no.
2,
pp.
1336–1340,
2015.
[33]
M.
M.
Najeeb,
“Multi-agent
system
for
hadith
processing,
”
International
J
ournal
of
Softwar
e
Engineering
and
its
Applications
,
v
ol.
9,
no.
9,
pp.
153–166,
2015,
doi:
10.14257/ijseia.2015.9.9.13.
[34]
A.
Bimba,
M.
A.
Ismail,
N.
Idris,
S.
J
aaf
ar
,
and
R.
Mahmud,
”T
o
w
ards
Enhancing
the
Compilation
of
Al-Hadith
T
e
xt
in
Malay
,
”
International
Pr
oceedings
of
Economics
De
velopment
and
Resear
c
h
v
ol.
83,
no.
March.
2015.
[35]
N.
Alias,
N.
A.
Rahman,
N.
K.
Ismail,
Z.
M.
Nor
,
and
M.
N.
Alias,
“Graph-based
te
xt
representati
on
for
Malay
translated
hadith
te
xt,
”
in
2016
3r
d
International
Confer
ence
on
Information
Retrie
val
and
Knowledg
e
Mana
g
ement,
CAMP
2016
-
Confer
ence
Pr
oceedings,
IEEE
,
2017,
pp.
60–66,
doi:
10.1109/INFRKM.2016.7806336.
Computational
methodolo
gies
for
sanad-based
hadith
analysis:
a
r
e
vie
w
(Abdelilah
Mhamedi)
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