Internati
o
nal
Journal of Ele
c
trical
and Computer
Engineering
(IJE
CE)
V
o
l.
6, N
o
. 3
,
Ju
n
e
201
6, p
p
. 1
011
~ 10
22
I
S
SN
: 208
8-8
7
0
8
,
D
O
I
:
10.115
91
/ij
ece.v6
i
3.9
789
1
011
Jo
urn
a
l
h
o
me
pa
ge
: h
ttp
://iaesjo
u
r
na
l.com/
o
n
lin
e/ind
e
x.ph
p
/
IJECE
Survey:
Models
and Prot
ot
yp
es of Schema Matching
Edhy Su
tanta
1
, Re
tant
yo W
a
rd
oyo
2
, K
h
ab
ib
Mu
sto
f
a
2
, Edi
Winar
k
o
2
1
Doctoral Progr
am of Computer
Science
at Dep
a
r
t
ment
of Compu
t
er Sciences & El
ectronics Instru
mentations,
Universitas Gad
j
ah Mada
, Yog
y
a
k
arta, Indon
esia
2
Department of
Computer Scien
ce
&
Electronics
Instrumentation
,
Universi
tas Gadjah Mad
a
, Yogy
ak
arta, Indones
i
a
Article Info
A
B
STRAC
T
Article histo
r
y:
Received Dec 25, 2015
R
e
vi
sed M
a
r
2,
2
0
1
6
Accepted
Mar 16, 2016
Schem
a
m
a
tchin
g
is criti
cal prob
lem
with
in m
a
ny app
lic
ations to
integra
tio
n
of data
/inform
at
ion, to
ach
iev
e
interop
e
rabi
lit
y,
and oth
e
r c
a
se
s caused
b
y
sc
he
ma
t
i
c
he
te
roge
ne
ity
.
Sc
he
ma
ma
tching
evolved from manual way
on
a
specific domain
,
leading to
a
new m
odels and methods that are semi-
autom
a
ti
c and m
o
re gener
a
l
,
so it
is able
to
effe
ct
ivel
y dir
e
c
t
the
user within
generate a mapping among elements of tw
o the schema or ontologies better
.
This paper
is a
summ
ar
y
of
lit
e
r
ature
review on
models
and pro
t
oty
p
es on
schem
a
m
a
tchin
g
within
the
las
t
25
ye
ars to d
e
scribe th
e progr
ess of and
research
chalen
ge and opportu
nities on a n
e
w m
odels, m
e
thods, and/or
prototy
p
es.
Keyword:
Data in
tegration
Het
e
r
oge
ne
ous
dat
a
ba
se
Inform
atio
n
in
teg
r
ation
Schem
a
m
a
t
c
hing
m
odel
Schem
a
m
a
t
c
hing
p
r
ot
ot
y
p
e
Copyright ©
201
6 Institut
e
o
f
Ad
vanced
Engin
eer
ing and S
c
i
e
nce.
All rights re
se
rve
d
.
Co
rresp
ond
i
ng
Autho
r
:
Ed
hy
S
u
t
a
nt
a,
Doct
oral
Pr
og
r
a
m
of C
o
m
put
er Sci
e
nce,
Depa
rt
m
e
nt
of
C
o
m
put
er Sci
e
nces
& El
ect
r
o
ni
cs I
n
st
r
u
m
e
nt
at
i
ons,
Uni
v
ersitas Ga
dja
h
M
a
da,
S2/
S
3 B
u
i
l
d
i
n
g
4t
h
fl
oo
r,
Seki
p
Ut
ara,
B
u
l
a
k
s
um
ur,
Yo
gy
a
k
art
a
,
5
5
2
8
1
,
I
n
d
o
n
esi
a
.
Em
a
il: ed
h
y
_
s
st@akp
rind
.ac.i
d
, edh
y
_
sst
@yah
oo
.co
m
1.
INTRODUCTION
Schem
a
m
a
t
c
hi
ng i
ssue
s
t
h
at
have em
erged
si
nce t
h
e earl
y
198
0s i
s
t
h
e
fun
d
am
ent
a
l
pr
o
b
l
e
m
i
n
man
y
ap
p
licatio
n
s
for d
a
ta/inform
at
io
n
in
teg
r
ation
.
Si
m
p
l
y
, schem
a
m
a
tchi
n
g
i
s
ho
w t
o
co
nst
r
uct
a m
a
ppi
n
g
bet
w
ee
n t
h
e t
w
o el
em
ent
s
of the schem
a
or o
n
t
o
l
o
gi
es ha
ve
i
n
com
m
on [1]
.
Schem
a
m
a
t
c
hi
n
g
i
s
an im
port
a
nt
issu
e fo
r t
h
e i
n
tegratio
n
of in
fo
rm
atio
n
from
m
u
ltip
le
h
e
t
e
rog
e
n
e
ou
s sou
r
ces
[2
]. Sche
m
a
m
a
tch
i
n
g
is also
i
m
p
o
r
tan
t
t
o
realize in
terop
e
rab
ility an
d i
m
p
l
e
m
en
t th
e
in
teg
r
ation
of
d
a
ta fro
m
d
i
fferen
t app
licatio
n
s
[3
].
In
dee
d
, t
h
e sc
h
e
m
a
m
a
t
c
hi
ng
al
so use i
n
t
h
e schem
a
evol
ut
i
on a
nd
reu
s
e o
f
so
ft
war
e
[4]
.
Schem
a
m
a
t
c
hi
ng i
s
p
a
rt of th
e top
i
c o
f
En
terp
rise App
licatio
n
In
tegratio
n
(EAI) in
p
a
rticu
l
ar En
terp
rise Info
rm
atio
n
In
tegratio
n
(EII), is an
in
teg
r
ation
task
at
th
e b
a
ck
end
lev
e
l w
ith th
e ai
m
to
ov
erco
m
e
th
e
p
r
ob
lem
s
cau
sed
b
y
sch
e
matic
h
e
tero
g
e
n
e
ity [5
]. Th
e m
ean
in
g sch
e
m
a
t
i
c h
e
tero
g
e
n
e
ity
is th
e
d
i
fferen
ce n
a
m
i
n
g
in the sch
e
m
a
d
e
fi
n
itio
n,
i
n
cl
udi
ng
t
h
e t
y
pe, f
o
rm
at
, and
p
r
eci
si
o
n
o
f
dat
a
[6]
.
T
h
e
m
a
i
n
pr
ocess
of sc
hem
a
m
a
tchi
n
g
i
s
t
o
de
vel
o
p
m
a
ppi
n
g
a
n
d
m
a
t
c
hi
ng
bet
w
een el
em
ent
s
o
f
i
n
t
e
r
sc
hem
a
[7]
.
From
initial appea
r
a
n
ce until the
end of
2002, the schem
a
m
a
tc
hing proce
ss m
o
st still done
m
a
nual
l
y
[8]
.
An
d,
o
n
l
y
s
o
m
e
m
odel
s
t
h
at
have
been
de
v
e
l
ope
d
fo
r c
o
m
m
on d
o
m
a
i
n
an
d acc
o
r
di
ng
t
o
t
h
e
di
ffe
re
nt
ap
pl
i
cat
i
on an
d sc
h
e
m
a
l
a
ngua
ges
[5]
.
M
a
n
u
al
l
y
schem
a
m
a
t
c
hi
n
g
ha
s t
h
e d
i
sadva
nt
age
,
a
m
ong
ot
he
rs,
re
qui
re
s a l
o
ng
t
i
m
e
, bo
ri
n
g
,
an
d
no
t
pract
i
cal
l
y
i
f
ap
pl
i
e
d i
n
a c
a
se t
h
at
i
n
v
o
l
v
e m
a
ny
schem
a
[8]
.
Manually
m
o
dels are also expensive and most likely th
ere was an error, and the
r
efore neede
d
a ne
w method
whi
c
h i
s
a sem
i
-aut
om
ati
c
[6]
.
Schem
a
m
a
t
c
hi
ng i
s
an
e
x
citing re
searc
h
objects to
di
rect effectively
to the
u
s
er to
so
lv
e th
e
p
r
o
b
l
em
o
f
sch
e
m
a
m
a
tch
i
n
g
[3
]. Th
e
research on
sch
e
ma
m
a
tch
i
n
g
is still o
p
e
n
to
find
ing
sm
art
e
r way
s
t
o
de
vel
op m
odel
s
an
d s
o
ft
w
a
re, i
n
pa
rt
i
c
ul
ar o
n
t
h
e
com
b
i
n
e
d
u
s
e o
f
t
h
e m
e
t
hods al
ready
ex
ist [9
].
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
SN
:
2
088
-87
08
IJEC
E
V
o
l
.
6,
No
. 3,
J
u
ne 2
0
1
6
:
10
1
1
– 10
22
1
012
Th
e
p
a
p
e
r
p
r
ov
id
es a su
mm
a
r
y of th
e
resu
lts of th
e st
u
d
y
o
f
literature
d
e
p
i
ctin
g
t
h
e
d
e
velo
p
m
en
t of
m
odel
s
and pr
ot
ot
y
p
es o
n
sc
hem
a
m
a
t
c
hi
ng o
v
er t
h
e l
a
st
25 y
ears, as wel
l
as show
i
ng cha
n
ces o
f
an
d
challenges of research
on sche
m
a
m
a
tching. The rest
of
t
h
e
pape
r i
s
o
r
ga
n
i
zed as fol
l
ows
.
Sect
i
on
2 de
s
c
ri
be
s
so
m
e
b
a
sic co
n
cep
t of sch
e
ma
match
i
n
g
.
Sectio
n
3
illu
st
rates so
m
e
rev
i
ew o
n
th
e ex
istin
g
sch
e
m
a
match
i
n
g
researc
h
. Sect
i
on
4 ex
pl
ai
ns t
h
e fut
u
re
di
rec
t
i
on f
o
r
sch
e
m
a
m
a
t
c
hi
ng res
earch
. Fi
nal
l
y
, Sect
i
on 5 c
onc
l
udes
t
h
e pa
pe
r.
2.
SC
HEMA
MA
TC
HING CONC
EPPT
2.
1.
Schem
a
Matc
hing Defi
nition
The t
e
rm
of sc
hem
a
s
m
a
t
c
hi
ng
has
been
de
f
i
ned i
n
di
ffe
re
nt
way
s
by
t
h
e
ex
pert
s,
b
u
t
a
l
l
of t
h
em
have
si
m
i
l
a
r m
eani
ngs
. Acc
o
r
d
i
n
g t
o
[1
0]
,
schem
a
s
m
a
t
c
hi
n
g
i
s
a si
m
ilar j
o
b wi
t
h
m
a
t
c
hi
ng
, w
h
e
r
e
a
s t
h
e
[6]
,
[1
1]
-[
1
3
]
defi
nes a schem
a
s
m
a
t
c
hi
ng as a process t
o
f
i
nd t
h
e rel
a
t
i
o
nshi
p bet
w
een
el
em
ent
s
of the pai
r
schem
a
. The
g
o
al
o
f
m
a
t
c
hi
ng sc
hem
a
s i
s
g
i
ven i
n
p
u
t
t
w
o
di
f
f
ere
n
t
sc
he
m
a
s, an
d/
or
ad
di
t
i
onal
i
n
f
o
rm
at
i
on,
and i
n
p
u
t
sche
m
a
s
m
a
ppi
ng r
u
l
e
s, t
h
e
n
spec
i
f
y
m
a
ppi
ng re
sul
t
schem
a
s elem
ent
s
bot
h sc
hem
a
s aft
e
r veri
fi
ed
by
t
h
e
user
[
1
4
]
. Schem
a
m
a
tchi
n
g
pr
ocess i
n
v
o
l
v
e
s
t
w
o sc
hem
a
s or o
n
t
o
l
ogi
es
, o
n
e se
r
v
es as s
o
u
r
ce a
n
d t
h
e
ot
he
r as a
t
a
r
g
e
t
[1]
.
2.
2.
Schem
a
Matc
hing Clas
sific
a
ti
on
The s
c
hem
a
m
a
t
c
hi
ng
pr
oc
ess can
i
n
v
o
l
v
e a
wi
de
var
i
et
y
of al
go
ri
t
h
m
s
, eg. t
o
d
e
t
e
rm
i
n
e t
h
e
el
em
ent
s
t
o
be
m
a
t
c
hed, t
h
e t
r
ansf
o
r
m
a
ti
on
m
a
ppi
n
g
,
o
r
m
e
rgi
n
g
[
15]
.
B
a
sed
o
n
al
g
o
r
i
t
h
m
i
s
used
, t
h
e
m
odel
i
n
m
a
t
c
hi
ng sc
hem
a
can be
cl
assi
fi
ed i
n
t
o
se
veral
cat
e
g
o
r
i
e
s.
A cl
assi
fi
cat
i
on of sc
hem
a
m
a
t
c
hi
ng by
[
14]
,
[
1
6
]
con
s
i
s
t
s
of schem
a
-base
d
vs
. i
n
s
t
ance-
based
,
ele
m
en
t v
s
.
stru
cture
g
r
anu
l
arity, lin
gu
istic b
a
sed
,
co
n
s
t
r
ain
t
-b
ased, m
a
tch
i
n
g
card
i
n
ality, au
x
iliary
in
fo
rm
atio
n
,
as well as ind
i
v
i
du
al v
s
. com
b
in
atio
n
a
l.
A cl
assi
fi
cat
i
o
n acc
or
di
n
g
t
o
[3]
c
onsi
s
t
s
o
f
sch
e
m
a
base
d, i
n
st
ance
-base
d
,
an
d
re
use
ori
e
nt
ed.
A sc
hem
a
m
a
tchi
n
g
cl
assi
fi
c
a
t
i
on
by
[
17]
d
i
vi
des t
h
e m
o
d
e
l
base
d
on t
h
e l
e
vel
an
d t
y
pe of i
n
fo
r
m
at
i
on t
h
at
expl
o
r
ed i
n
cl
ude
el
em
ent
s
and st
ruct
u
r
e, a
nd
base
d o
n
t
h
e t
y
pe o
f
i
n
f
o
rm
at
i
on be
i
ng e
xpl
ore
d
i
n
cl
ude t
e
rm
i
nol
ogy
(i
nv
ol
vi
ng
aspect
s of l
i
n
g
u
i
s
t
i
c
s (co
n
si
st
s of l
a
n
g
u
age
-
base
d
and l
i
n
g
u
i
s
t
i
c
-
b
ase
d
)
or i
n
v
o
l
v
e as
pect
s o
f
l
i
n
g
u
i
s
t
i
c
s (st
r
i
n
g-
base
d )
)
,
bas
e
d o
n
st
r
u
ct
u
r
a
l
aspect
s (i
ncl
u
di
n
g
t
h
e i
n
t
e
r
n
al
as
pect
(c
o
n
st
rai
n
t
base
d) a
n
d
r
e
l
a
t
i
onal
(c
on
s
i
st
s of
al
i
gnm
ent
re
use,
g
r
a
p
h
-
based
,
t
a
x
o
nom
y
base
d, a
n
d re
p
o
si
t
o
ry
st
ruct
ur
e))
)
, a
n
d sem
a
nt
i
c
s (c
on
si
st
s
of
u
p
p
er l
e
vel
f
o
rm
al
ont
ol
ogy
an
d m
odel
b
a
s
e
d)
.
In
[1]
,
t
h
e sc
he
m
a
m
a
t
c
hi
ng m
odel
cl
assi
fi
ed by
le
vel com
p
one
n
ts that are m
a
tched (c
oncept
u
al and
st
ruct
u
r
e)
, t
h
e
l
e
vel
of use
r
i
n
t
e
rve
n
t
i
o
n
(
m
anual
an
d
aut
o
m
a
ti
c), t
h
e m
e
t
hod
us
ed (st
a
n
d
-
al
o
n
e
and
com
b
i
n
ed)
,
a
n
d t
h
e
t
y
pe
of
com
pone
nt
s
us
ed as
t
h
e
basi
s f
o
r
m
a
t
c
hi
ng (
u
si
n
g
t
h
e sc
hem
a
or sc
he
m
a
and
i
n
st
ance)
. Acc
o
r
d
i
n
g t
o
[1
8]
a schem
a
m
a
t
c
hi
n
g
m
odel
s
consi
s
t
s
of R
S
M
(rel
a
t
i
ons
sc
hem
a
s
m
a
t
c
her
)
, A
N
M
(relatio
ns attrib
u
t
e n
a
m
e
m
a
t
c
h
e
r), DTM (data typ
e
matc
her), CM
(co
n
st
raint
m
a
tcher),
and I
D
M
(inst
a
nce of
dat
a
m
a
t
c
her).
In di
ffe
rent
wa
y
s
, [5]
gr
o
upi
n
g
schem
a
m
a
t
c
hi
n
g
al
go
ri
t
h
m
i
n
t
o
t
h
ree t
y
p
e
, nam
e
ly
li
ng
ui
st
i
c
matcher (NTA (nam
e, conne
cted term
s, attributes) lingu
istic
m
a
tch
e
r, prefix
/suffi
x
b
a
sed
m
a
tch
e
r for
n
a
m
e
,
and
p
r
efi
x
/
s
u
f
f
i
x ba
sed
m
a
t
c
h
e
r f
o
r t
y
pes
)
,
v
o
cab
ul
ar
m
a
t
c
hers
(
W
o
r
dNet
-base
d
w
o
r
d
m
a
t
c
her
fo
r
nam
e
s an
d
NTA (nam
e, connected term
s, attributes) related te
rm
s sim
ilarity
), and
str
u
ctu
r
al m
a
tchers
(fl
oo
din
g
si
m
ilarit
y
, Wo
rd
Net
-
b
a
sed
ancesto
r con
t
ex
t si
m
ilarit
y
, st
rin
g
co
m
p
arison
b
a
sed
ch
ild
con
t
ex
t similarity
, ch
ild
co
n
t
ex
t si
m
ila
rity, an
d
th
e d
i
rect an
cestor si
milarity
usi
ng st
ri
ng c
o
m
p
ari
s
on
). Si
m
i
l
a
r wi
t
h
[1
4]
,[
16]
, a
sch
e
m
a
match
i
n
g
classification
is g
i
v
e
n
b
y
[9
], th
ere are cov
e
ri
n
g
lin
gu
istic
m
a
tch
i
n
g
,
aux
iliary in
form
a
tio
n,
i
n
st
ance-
base
d
m
a
t
c
hi
ng, st
r
u
ct
u
r
e-
base
d m
a
t
c
hi
ng, co
n
s
t
r
ai
nt
-
b
ased
m
a
t
c
hi
ng, r
u
l
e
based m
a
t
c
hing
, an
d
hy
b
r
i
d
m
a
t
c
hi
ng.
In a
not
her
ref
e
rence
,
[
19]
c
l
assi
fi
es t
h
e sche
m
a
m
a
t
c
hing m
odel
s
i
n
t
o
t
w
o cat
ego
r
i
e
s, nam
e
l
y
schem
a
-based
and i
n
stance
-based.
A schem
a
based c
o
ns
ists of elem
ent-based and struct
ure
-
ba
sed. Element-
base
d c
o
n
s
i
s
t
s
o
f
l
i
n
g
u
i
s
t
i
c
-base
d
a
n
d
c
o
n
s
t
r
ai
nt
-
b
ased
,
whi
l
e
t
h
e st
r
u
ct
ure
base
d i
s
de
vel
o
pe
d
ba
sed
o
n
con
s
t
r
ai
nt
-
b
ase
d
. I
n
st
a
n
ce-
bas
e
d m
odel
are d
e
vel
o
ped
base
d
on
th
e elem
e
n
t lev
e
l wh
ich
co
nsists o
f
lingu
istic
base
d, c
o
nst
r
ai
nt
-
b
ased
an
d l
e
arni
ng
-ba
s
ed
.
Accord
ing
to [9
], sev
e
ral o
t
h
e
r m
o
d
e
ls ev
er d
e
v
e
lop
e
d
in
cl
u
d
i
n
g
th
o
s
e u
tilizin
g
ad
d
ition
a
l
i
n
f
o
rm
at
i
on,
n
a
m
e
l
y
grap
h m
a
t
c
hi
ng
,
usa
g
e-
base
d m
a
t
c
hi
ng,
d
o
cum
e
nt
c
ont
e
n
t
si
m
i
l
a
ri
ty
, an
d
d
o
cum
e
nt
l
i
n
k
sim
i
l
a
ri
ty
. A cl
assi
fi
cat
i
on com
b
i
n
at
i
ons
m
odel
of s
c
hem
a
m
a
t
c
hing f
o
r l
a
rge
schem
a
i
s
cove
ri
n
g
in
d
e
p
e
nd
en
t st
r
a
teg
y
or
sequ
en
ce ex
ecu
tio
n or
co
m
b
in
atio
n
o
f
ex
ecu
tio
n,
p
a
rallel m
a
tch
i
n
g
,
self-tun
i
ng
match work
flow, ea
rly search space
pruning,
par
tition-base
d m
a
tchi
ng, and sc
he
ma opti
m
izati
on
[9].
Schem
a
m
a
t
c
hi
ng m
odel
i
n
a
part
i
c
ul
a
r
dom
ai
n, i
.
e. re
use b
a
sed m
a
t
c
hi
ng and
hol
i
s
t
i
c
m
a
t
c
hi
ng i
s
a di
f
f
ere
n
t
app
r
oach
[9]
.
Som
e
st
rat
e
gi
es t
h
at
i
n
cl
ude i
n
t
e
ract
i
o
n an
d
feed
bac
k
fr
om
users i
n
t
h
e m
a
t
c
hi
ng
pr
oces
s have
also bee
n
de
velope
d, i
n
cluding
GU
I (gra
phical user
i
n
terface) suppor
t, inc
r
em
enta
l
m
a
tching, T
o
p-k
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
ECE
I
S
SN
:
208
8-8
7
0
8
Sur
vey:
Model
s
and Pr
ototy
p
es of
Sc
hema
Matchi
ng (Edhy Sutanta)
1
013
m
a
t
c
hi
ng, a
nd
col
l
a
bo
rat
i
v
e [
9
]
.
An
ot
her st
r
a
t
e
gy
i
s
t
h
e use of sem
a
nt
i
c
m
a
t
c
hi
ng al
g
o
r
i
t
h
m
s
t
o
ext
e
nd, f
o
r
ex
am
p
l
e se
m
a
n
tic tag
g
i
ng
and
co
nd
itio
nal tag
g
i
n
g
[9
].
2.
3.
User I
n
ter
v
ention
Th
e m
a
in
prob
lem
in
th
e sch
e
m
a
m
a
tch
i
n
g
is
o
f
ten
foun
d n
a
m
i
n
g
t
h
e sch
e
m
a
th
at is no
t clear,
d
i
f
f
i
cu
lty f
o
u
n
d
synon
ym
o
u
s
in
n
a
m
i
n
g
,
o
r
d
i
f
f
e
r
e
n
ces in sch
e
m
a
d
e
f
i
n
i
tio
n
lang
uag
e
[
6
]. Thu
s
th
e sch
e
m
a
matching m
odel is not likely to produce mapping sche
ma that is
100% accurate accorda
n
ce with us
e
r
expect
e
d
[
6
]
.
For t
h
ese reas
ons
, t
h
e sc
hem
a
m
a
t
c
hi
ng cann
o
t
be f
u
l
l
y
do
ne aut
o
m
a
t
i
cal
l
y
, usual
l
y
m
u
st
be
corrected
by t
h
e use
r
to obtain
th
e co
rrect fi
nal resu
lts
[15
]
.
Th
ere are two cases in
wh
i
c
h
th
e sch
e
ma
m
a
tc
h
i
n
g
will ex
p
e
rien
ced
failure an
d
requ
ire
u
s
er
in
vo
lv
em
en
t. First,
wh
en
t
h
e s
o
urce elem
ent schem
a
cannot
be
m
a
tche
d by
any elem
ent in t
h
e targets sc
hem
a
using the rule
s used, or sec
o
nd, if the s
o
urce elem
en
t
schem
a
prod
uc
es som
e
of t
h
e el
em
ent
s
whi
c
h are
co
nsid
ered su
it
ab
le in th
e targets sch
e
m
a
an
d th
e system
can
no
t
d
e
term
in
e th
e
b
e
st
fit elemen
ts au
to
m
a
tically
[2
0]
.
Acco
r
d
i
n
g t
o
[2
1]
, ge
ne
ral
l
y
m
a
t
c
hi
ng t
h
e
t
w
o sc
hem
a
s req
u
i
r
es i
n
f
o
r
m
at
i
on t
h
at
i
s
not
al
way
s
av
ailab
l
e in
the sch
e
m
a
an
d
can
no
t b
e
done au
to
m
a
tica
l
l
y
, so
it r
e
q
u
i
res th
e in
vo
lv
emen
t o
f
th
e user
s to
revi
e
w
an
d de
t
e
rm
i
n
e sug
g
e
s
t
i
ons o
n
sche
m
a
m
a
t
c
hi
ng r
e
sul
t
s
. Schem
a
m
a
t
c
hi
ng pr
o
cess coul
d ne
v
e
r be
d
o
n
e
au
to
m
a
tic
ally fu
lly th
ere
is a co
m
p
lete se
m
a
n
tic
m
a
tch
i
n
g
m
o
d
e
l for t
h
e inform
atio
n
syste
m
s in
teg
r
atio
n
[2
2]
. A
not
her
reaso
n
t
h
at
ca
uses t
h
e sc
he
m
a
m
a
t
c
hi
ng c
a
nn
ot
be
do
ne
aut
o
m
a
t
i
call
y
is t
h
e nam
i
ng con
f
l
i
c
t
an
d lev
e
ls
o
f
ab
straction
con
f
lict [4
].
2.
4.
Indi
vi
du
al
vs Com
b
i
n
a
t
ori
a
l
Ma
tchers
Schem
a
m
a
t
c
hi
n
g
m
odel
s
can be de
vel
ope
d usi
n
g
t
h
e
i
ndi
vi
d
u
al
or
com
b
i
n
at
i
o
nal
m
a
t
c
hers
[1
4]
,[
1
6
]
.
I
n
di
vi
d
u
al
m
a
t
c
her fast
er
i
n
p
r
oc
e
ss o
f
c
o
m
p
l
e
t
i
on
,
but
has
t
h
e
di
sa
dva
nt
age
i
s
o
n
l
y
ap
p
r
o
p
ri
at
e i
n
certain
cases,
so
th
at
g
e
n
e
rally requ
ir
es m
o
re than
one m
a
tcher
which c
o
m
b
i
n
ed [1
9]
.
Acco
r
d
i
n
g t
o
[
16]
,
[
1
9
]
,
th
e
comb
ina
tion
a
l
ma
tch
e
rs
can
b
e
im
p
l
e
m
e
n
ted
as co
m
p
osite o
r
h
ybrid
.
Th
e term
o
f
com
p
o
s
ite
m
a
tch
e
rs is
syn
o
n
y
m
o
u
s
with
in
ter-m
atch
er
p
a
rallelis
m
,
wh
ile
h
ybrid
match
e
r
is
syno
n
y
m
o
u
s
with
in
tra-p
a
rallelism
[2
3
]
.
Hyb
r
i
d
m
o
d
e
ls u
s
es m
u
ltip
le criteria sim
u
lta
n
e
ou
sly m
a
tch
i
n
g
[2
0
]
,[24
],[2
5
]
, wh
ile com
p
o
s
ite
m
a
tch
e
rs
run
sep
a
rately or i
n
d
e
p
e
n
d
e
n
t
al
go
rith
m
s
an
d com
b
in
es at th
e resu
lts [26
]
.
Th
us,
hy
bri
d
m
a
t
c
her com
b
i
n
es t
w
o
di
f
f
e
rent
m
e
t
hods
are p
r
oces
se
d si
m
u
l
t
a
neou
s, w
h
ereas
com
posi
t
e
m
a
t
c
her c
o
m
b
i
n
e t
w
o m
e
t
hod
s
t
h
at
are
pr
o
cessed i
n
a s
e
que
nce t
h
at
i
s
a
m
e
t
hod
t
o
b
e
im
pl
em
ent
e
d aft
e
r t
h
e
ot
he
r
m
e
t
hod
i
s
c
o
m
p
l
e
t
e
d.
Acc
o
r
d
in
g to
[1
6
]
h
ybrid
m
a
tch
e
r is t
o
co
m
b
in
e m
o
re than
one m
e
t
hod si
m
u
lt
aneou
s
l
y
to per
f
o
rm
m
a
tchi
n
g
bet
w
ee
n
t
h
e schem
a
el
em
ent
s
, and s
h
o
u
l
d
gi
ve bet
t
e
r
resul
t
s
and
i
m
prove
d
per
f
o
r
m
a
nce (e
ffect
i
v
e
n
ess
)
ra
t
h
er t
h
an
i
n
di
vi
dual
m
a
t
c
her.
3.
SC
HEMA
MA
TC
HING MOD
ELS AND PROTOT
YPES
The st
u
d
y
fo
un
d at
l
east
34
m
odel
s
an
d p
r
ot
ot
y
p
es o
n
sche
m
a
m
a
t
c
hi
ng i
n
7
1
sci
e
nt
i
f
i
c
pu
bl
i
cat
i
o
n
s
t
h
at
are
rel
e
va
nt
i
n
t
h
e l
a
st
2
5
y
ears
.
M
o
del
s
an
d
pr
ot
ot
y
p
e
s fi
rst
on
sc
he
m
a
m
a
t
c
hi
ng i
s
SEM
I
NT
[2
7]
, w
h
i
l
e
t
h
e l
a
t
e
st
i
s
C
O
M
A
3.
0
[2
8]
. Each
o
f
m
o
d
e
l
s
and
p
r
ot
ot
y
p
es m
a
y
use an i
n
p
u
t
sche
m
a
s such a
rel
a
t
i
onal
m
odel
(R
DF/
R
el
at
i
onal
Dat
a
base F
o
rm
at
), XM
L m
odel
(DT
D
/
D
oc
um
ent
Ty
pe De
fi
n
i
t
i
on, o
r
X
S
D/
W3
C
XM
L Schem
a
), o
r
o
n
t
o
l
ogy
(O
WL/
W
e
b
Ont
o
l
o
gy
Lan
gua
ge
). P
r
ot
ot
y
p
e schem
a
s
m
a
t
c
hi
ng has
bee
n
eval
uat
e
d ext
e
nsi
v
el
y
by
[3
]
,
[8]
,
[
2
9]
, and
t
h
e resul
t
s
sho
w
e
d
t
h
at
m
o
st
schem
a
s
m
a
t
c
hi
ng p
r
ot
ot
y
p
e
d
e
v
e
l
o
p
e
d
for
a li
mited
sco
p
e o
r
sp
ecific, an
d
so
m
e
o
t
h
e
rs th
ere
were
dev
e
lop
e
d
sp
eci
fically b
y
u
tili
zin
g
the
ont
ol
o
g
y
.
R
e
fers t
o
[
1
8]
,[3
0
]
,
[
3
1]
, t
h
e
st
udy
[
4
]
wa
s
t
o
i
n
t
e
g
r
at
e he
t
e
ro
gene
o
u
s
d
a
t
a
base ba
sed
on
sem
a
nt
i
c
ont
ol
o
g
y
,
a
nd t
e
st
ed o
n
aca
de
m
i
c dat
a
base t
h
at
i
s
de
fi
ne
d
usi
n
g M
S
Acc
e
ss an
d M
y
SQ
L. Di
sc
ove
ry
a
m
odel
fo
r co
n
n
ect
i
ng
bet
w
ee
n dat
a
s
o
u
r
ces
on t
h
e
web
by
[
32]
,
[
3
3
]
i
s
al
so a res
earch a
r
ea i
n
whi
c
h de
vel
o
p
m
ent
of
a sem
a
n
tic ap
pro
a
ch
t
o
t
h
e
relatio
n
s
h
i
p
b
e
tween
en
tities is
req
u
i
red and is
v
a
lid
for
wid
e
sp
read
av
ailab
i
l
ity o
f
ont
ol
o
g
y
.
Furt
herm
ore, r
e
fers t
o
[
28]
, t
h
e st
udy
[
9
]
has st
udi
ed p
r
ot
o
t
y
p
es schem
a
m
a
t
c
hi
ng t
h
at
devel
ope
d i
n
th
e year
20
01-
201
1
an
d
t
h
en
co
m
p
ar
ed
pr
o
t
o
t
yp
es (
C
up
id
, C
O
MA
++, A
S
M
O
V,
Falco
n
-A
O, RiMO
M,
AGRE
E
MENTMAKER
,
and Ope
n
II)
on aspects of a
r
chit
ect
ure ,
sc
hem
a
represe
n
t
a
t
i
on, re
p
r
ese
n
t
a
t
i
on
schem
a
m
a
ppi
ng
, i
n
f
o
rm
ati
on an i
n
p
u
t
an
d m
a
t
c
hi
ng al
go
ri
t
h
m
s
, as wel
l
as t
h
e ex
ecut
i
on m
a
t
c
hi
ng
o
n
schem
a
el
em
ent
.
Acc
o
r
d
i
n
g t
o
[
9
]
,
pr
ot
ot
y
p
e
s usi
n
g di
f
f
er
ent
app
r
oache
s
i
n
t
h
e
m
a
t
c
hi
ng al
go
ri
t
h
m
bet
w
ee
n
sch
e
m
a
ele
m
e
n
ts,
wh
ile COMA ++, Falco
n
-AO, RiMO
M, and
Agree
m
entMaker
i
s
t
h
e
pr
ot
ot
y
p
e
t
h
at
com
b
i
n
es t
h
re
e
m
e
t
hods, i
e
l
i
ngui
st
i
c
, st
r
u
ct
ure, a
nd i
n
st
ance-
base
d. U
s
e of ext
e
r
n
al
di
ct
i
ona
ri
es, s
u
ch as
th
esaurus are gen
e
rally u
s
ed
t
o
im
p
r
ov
e accu
r
acy
o
f
m
a
tch
i
n
g
lingu
istic.
Lim
i
t
e
d GUI s
u
p
p
o
rt
has
bee
n
pr
o
v
i
d
e
d
by
several
p
r
ot
ot
y
p
es [
34]
,
[
2
9
]
,
and
part
i
a
l
l
y
are abl
e
t
o
do
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
SN
:
2
088
-87
08
IJEC
E
V
o
l
.
6,
No
. 3,
J
u
ne 2
0
1
6
:
10
1
1
– 10
22
1
014
on t
w
o sc
hem
a
ont
ol
o
g
y
m
a
t
c
hi
ng
[
1
]
,
[
35]
,[
36]
. Se
ve
ral
pr
ot
ot
y
p
es
we
re pa
rt
i
c
i
p
at
e i
n
O
A
EI
(O
nt
ol
o
g
y
Alig
n
m
en
t Evalu
a
tio
n
In
itiativ
e) in
creased sig
n
i
fican
tly, b
u
t
still n
e
ed to
b
e
d
e
v
e
lop
e
d
t
o
o
v
e
rcome th
e
p
r
ob
lem o
f
sch
e
m
a
match
i
n
g
on
a wid
e
r sco
p
e
[37
]
. Soph
isticated
tech
n
i
qu
es su
ch
as p
a
rtitio
n
i
ng
sch
e
m
a
,
p
a
rallel m
a
tch
i
n
g
,
reu
s
e in m
a
p
p
i
n
g
an
d self-tun
ing
cap
a
b
ilities (e.g
.,
d
y
n
a
m
i
c
match
i
n
g
o
p
t
i
o
n
s
) i
s
su
ppo
rted
to a
li
mited
ex
ten
t
[2
8
]
.
M
odel
s
an
d p
r
ot
ot
y
p
es o
f
hy
bri
d
schem
a
mat
c
hi
ng e
v
er d
e
vel
o
ped ea
rl
i
e
r was C
L
I
O
[3
8]
-[
4
2
]
,
an
d
researc
h
by
[
4
3]
.
Whi
l
e
SE
M
I
NT
[2
5]
,[
2
7
]
,
[
44]
, L
S
D
[
26]
, t
h
e C
u
pi
d
[1
4]
, C
O
M
A
[4
5]
, C
O
M
A
++ [3]
,
C
O
M
A
3
.
0 [
2
8]
,[
46]
, IM
AP
[47]
, PR
OTO
P
LASM
[
4
8]
-[
51]
, F
A
LC
O
N
-
A
O
[
6
]
and [
52]
, as wel
l
as t
h
e
ASM
O
V
[
53]
was
de
vel
o
ped
usi
n
g
a c
o
m
b
inat
i
o
n
o
f
m
e
t
hods
as c
o
m
posi
t
e.
Lin
g
u
i
sitic b
a
sed
m
a
tch
i
n
g
meth
od
u
s
ed
o
n
DIKE
[54
]
-[56], MOMIS [57],[58
], ONION [59
]
-[61
],
ARTEM
I
S
[
6
2
]
,
UN
IF
ORM
[6
3]
, WIS
E-
IN
TEGR
A
TOR [
2
]
,
[
64]
,
[
6
5
]
,
P
R
OM
PT [6
6]
,[
67]
,
R
O
ND
O [2
1]
,
OLA
[6
8]
, Q
o
m
[69]
,[
70]
, S
-
M
A
TC
H [6
2]
,[
71]
,
[
7
2
]
,
R
i
M
O
M
[1
0]
, A
G
R
EEM
E
N
T M
AKER
[
1
]
,
OPE
N
I
I
[3
6]
an
d [
4
]
.
Whi
l
e
D
ELT
A [
73]
,
[
7
4
]
,
s
i
m
i
l
a
ri
t
y
fl
ood
i
ng
[7
5]
, XC
LUST
[7
6]
, a
nd
resea
r
ch
b
y
[77]
im
pl
em
ent
s
t
h
e st
ruct
ure
-
ba
se
d m
a
t
c
hi
ng m
e
t
h
o
d
.
Usage constrai
nt in the on sc
he
m
a
matching assum
e
s th
at c
onst
r
aint has a meaning to set a sim
i
larity
dat
a
base
el
em
ent
,
fo
r e
x
am
pl
e, at
t
r
i
b
ut
e
AT
1 i
n
t
a
bl
e
X is
defi
ned as a
c
h
aracter
was
sa
me as attribute
AT
2
i
n
t
h
e t
a
bl
e Y
whi
c
h i
s
de
fi
n
e
d as a t
e
xt
[
3
1]
. Acc
o
r
d
i
n
g
t
o
[1
9]
, t
h
e
use
of c
onst
r
ai
nt
-
b
ase
d
i
s
part
o
f
m
odel
gr
o
up
o
n
sc
he
m
a
m
a
t
c
hi
ng whi
c
h i
s
i
n
cl
u
d
ed i
n
l
e
vel
s
t
ruct
u
r
e,
b
u
t
n
o
t
desc
ri
be
d i
n
m
o
re ab
out
what
pr
o
p
ert
i
e
s
whi
c
h e
xpl
ore
d
a
n
d i
n
cl
ude
d as
con
s
t
r
ai
nt
.
I
n
st
ance-
base
d m
e
t
h
o
d
i
s
use
d
o
n
TR
AN
SC
M
[2
0]
,
Aut
opl
e
x
[7
8]
,
Aut
o
m
a
t
c
h [7
9
]
-[8
1]
,
GLU
E
[
82]
,
[
8
3
]
,
SC
M
[8
4]
, as
wel
l
as
D
U
M
A
S
[
8
5]
.
Aux
iliary b
a
sed
m
a
tch
i
n
g
such
a d
i
ction
a
ry,
W
o
rdNe
t,
or Co
rpu
s
are
u
s
ed
on
DIKE [54
]
-[56
],
M
O
M
I
S [
5
7]
,[
58]
,
ON
IO
N [
59]
-
[
61]
, AR
T
E
M
I
S [
62]
, t
h
e C
upi
d
[1
4,
8
0
, 8
1
]
,
C
O
M
A
[4
5]
, XC
L
U
S
T
[7
6]
,
UN
IF
ORM
[6
3]
, WI
SE-
I
N
TEG
RATOR [2
]
,
[4
9]
,[
64]
,
[
6
5
]
,
OL
A [5
0]
,[
6
8
]
,
S-M
A
TC
H
[
5
1
]
,
[
62
],
[7
1
]
,[7
2
]
,[
77
],
CO
M
A
++ [3
],
[6
],
O
P
EN
II
[36
]
,
as w
e
ll as th
e CO
MA
3
.
0
[28
]
.
Th
e sur
v
ey
r
e
su
lts
sh
ow
A
u
x
iliary b
a
sed
m
a
tch
i
n
g
su
ch
a d
i
ctio
n
a
r
y
,
W
o
r
d
Net, o
r
Corp
u
s
ar
e u
s
ed
on
DIK
E
[5
4
]-[5
6
]
,
MO
MI
S
[5
7]
,[
5
8
]
,
O
N
I
O
N
[
5
9]
-[
6
1
]
,
AR
TEM
IS
[6
2]
, t
h
e C
u
p
i
d [
14]
,
[
8
0
]
,
[
8
1]
, C
O
M
A
[
4
5]
, XC
L
U
ST
[7
6]
,
UN
IF
ORM
[6
3]
, WI
SE-
I
N
TEG
RATOR [2
]
,
[4
9]
,[
64]
,
[
6
5
]
,
OL
A [5
0]
,[
6
8
]
,
S-M
A
TC
H
[
5
1
]
,
[
62
],
[7
1
]
,[7
2
]
,[
77
],
CO
M
A
++ [3
],
[6
],
O
P
EN
II
[3
6
]
,
as w
e
ll as th
e CO
MA
3.
0
[
28].
I
n
d
e
tail,
th
e su
rv
ey
resul
t
s
s
h
o
w
t
h
e com
p
ari
s
o
n
of t
h
e m
e
t
hods
use
d
i
n
t
h
e m
odel
a
n
d p
r
ot
ot
y
p
es o
n
sc
hem
a
m
a
t
c
hi
ng s
h
o
w
n i
n
Tabl
e 1.
Tabl
e
1. T
h
e
c
o
m
p
ari
s
on
o
f
t
h
e m
e
t
hods
us
ed i
n
t
h
e m
ode
l
and
p
r
ot
ot
y
p
e
s
o
n
sc
hem
a
m
a
t
c
hi
ng
Pr
ototy
p
e
Na
m
e
Author
/Researcher
I
nput
Using M
e
thod [9]
Do
m
a
in
GUI
R
D
F
DTD
/X
S
D
O
W
L
Lingu-
istic
based
St
r
u
c
-
ture
based
Co
n
s
t
-
raint
based
Insta-
nce
based
Method
co
m
b
-
ina
t
ion
Di
ct
ion
ary/
Wo
rd
N
et
/Cor
pu
s
Sp
e-
cif
ic
Ge
ne
-
ral
Hy
-
bri
d
Co
m
po-
site
SEMINT
Li &
Clifton
(
1994)
Li &Clifton
(
2000)
Li
et al
.
(
2000)
DELTA
Benkley
et al
.
(
199
5)
Clift
on
et al
.
(
199
6)
TRANSCM
Milo & Zohar
(
199
8)
DIKE
Palopoli
et al
.
(
199
8)
Palopoli
et al
.
(
199
9)
Palopoli
et al
.
(
200
0)
MO
MIS
Castano &
Antonellis
(
1999)
Berga
m
aschi
et al
.
(
2001)
ONI
O
N
Mitra
et al
.
(
1999)
Mitra
et al
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
ECE
I
S
SN
:
208
8-8
7
0
8
Sur
vey:
Model
s
and Pr
ototy
p
es of
Sc
hema
Matchi
ng (Edhy Sutanta)
1
015
Pr
ototy
p
e
Na
m
e
Author
/Researcher
I
nput
Using M
e
thod [9]
Do
m
a
in
GUI
R
D
F
DTD
/X
S
D
O
W
L
Lingu-
istic
based
St
r
u
c
-
ture
based
Co
n
s
t
-
raint
based
Insta-
nce
based
Method
co
m
b
-
ina
t
ion
Di
ct
ion
ary/
Wo
rd
N
et
/Cor
pu
s
Sp
e-
cif
ic
Ge
ne
-
ral
Hy
-
bri
d
Co
m
po-
site
(
2000)
Mitra
&
W
i
eder
hold
(
2002)
ARTEMIS
Giunchiglia
et
al
.
(
2005)
CUPID
M
a
dhavan
et
al
.
(
2001)
LSD
Doan
et al
.
(
2001)
AUTOPLE
X
Berlin &
M
o
tr
o (
2001)
AUTOMAT
CH
Berlin &
M
o
tr
o (
2002)
CLIO
Hernández
et
al
.
(
2001)
Nau
m
ann
et
al
.
(
2002)
Popa
et al
.
(
2002)
Haas
et al
.
(
2005)
XCLUST
Lee
et
al
.
(
2002)
COMA
Do dan Rah
m
(
2002)
GL
UE
Doan
et al
.
(
2002)
Si
m
ilari
ty-
f
l
ooding
Melnik
et al
.
(
2002)
UNIF
O
RM
Palopoli
et al
.
(
2002)
WISE-INTE
GRAT
OR
He
et al
.
(
2003)
He &
Chang
(
2003)
He
et al
.
(
2004)
PROM
PT
Noy
&
Musen
(
2003)
Noy
&
Musen
(
2004)
RON
D
O
Melnik
et al
.
(
2003)
P
r
ot
oplas
m
Bernstein
et
al
.
(
2004)
IMAP
Dham
ankar
et
al
.
(
2004)
OL
A
Euzenat
et al
.
(
2004)
QOM
Ehrig & S
t
aab
(
2004)
Eh
rig
&
S
u
r
e
(
2004)
SCM
Hoshiai
et al
.
(
2004)
S-MATC
H
Giunchiglia
et
al
.
(
2004)
Giunchiglia
et
al
.
(
2005)
DUMAS
Bilke &
Nau
m
ann
(
2005)
FALC
ON-
AO
Jian
et al
.
(
2005)
COMA++
Do (
2005)
RiMOM
Li
et al
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
SN
:
2
088
-87
08
IJEC
E
V
o
l
.
6,
No
. 3,
J
u
ne 2
0
1
6
:
10
1
1
– 10
22
1
016
Pr
ototy
p
e
Na
m
e
Author
/Researcher
I
nput
Using M
e
thod [9]
Do
m
a
in
GUI
R
D
F
DTD
/X
S
D
O
W
L
Lingu-
istic
based
St
r
u
c
-
ture
based
Co
n
s
t
-
raint
based
Insta-
nce
based
Method
co
m
b
-
ina
t
ion
Di
ct
ion
ary/
Wo
rd
N
et
/Cor
pu
s
Sp
e-
cif
ic
Ge
ne
-
ral
Hy
-
bri
d
Co
m
po-
site
(
2009)
Agree
m
e
nt
-
ma
ke
r
Cruz
et al
.
(
2009)
ASMOV
Jean-Mar
y
et
al
.
(
2009)
SYM
Chien &
He
(
2010)
OPE
N
I
I
Selig
m
a
n
et
al
.
(
2010)
COMA 3.0
Rah
m
,
et
al
.
(
2011)
---
L
a
r
s
on
et al
.
(
1989)
---
Hayne &
Ram
(
1990)
---
Gotthard
et
al
.
(
1992)
---
Spaccapietra
& Pa
ren
t
(
1992)
---
L
e
r
n
er
(
2000)
---
Mitra
et al
.
(
2000)
---
Castano
et al
.
(
2001)
---
M
a
dhavan
et
al
.
(
2003)
---
Kang &
Naughton
(
2003)
---
Ber
tino
et al
.
(
2004)
---
Em
bl
e
y
et al
.
(
2004)
Xu &
E
m
ble
y
(
2003)
---
W
a
ng
et al
.
(
2004)
---
Dr
agut &
Lawren
c
e
(
2004)
---
Mo
rk
&
Bernstein
(
2004)
---
Lu
et al
.
(
2005)
---
Tu
&
Yu
(
2005)
---
E
n
g
m
ann &
M
a
ss
m
a
nn
(
2007)
---
Kavitha
et al
.
(
2011)
St
at
i
s
t
i
call
y
, research
, m
odel
s
, an
d sc
hem
a
m
a
t
c
hi
ng p
r
ot
ot
y
p
es t
h
at
de
vel
o
ped i
n
t
h
e
l
a
st
25 y
ear
s
cl
assi
fi
cat
i
on
b
a
sed
on
i
n
put
s
and
t
h
e m
e
t
h
o
d
used
(
r
efe
r
s t
o
[
9
]
s
h
o
w
n
on
Tabl
e
2.
Acc
o
rdi
ng t
o
Ta
bl
e
2, i
t
i
s
k
nown th
at lin
gu
istic-b
ased m
e
th
o
d
s m
o
st wid
e
ly app
lied
in th
e m
o
d
e
l on
sch
e
m
a
m
a
tch
i
n
g
i.e. 76%,
followe
d by a
com
b
ination
of
m
e
thods
a
s
com
posite is 73%, auxiliary base
d i.e.
56%,
followe
d by structure-
base
d an
d i
n
st
ance-
base
d i
.
e.
49%
, co
nst
r
ai
nt
-
b
ased
was
20%, and the
least used wa
s
a com
b
ination of
m
e
thods as hybri
d
i.e. 13% . Utilization
of
l
i
nguistic-b
ase
d
m
e
thods
are
relatively
m
o
st rapi
dly, it is growi
n
g
in
lin
e with scien
ce
d
e
v
e
l
o
p
m
en
t and
o
n
t
o
l
og
y in
creasi
ng
ly wid
e
sp
read
av
ailab
ility, n
e
verth
e
less ex
p
l
oration
m
e
thods
base
d and instance-based st
ruct
ure i
s
still being c
o
nducted by t
h
e
researc
h
ers.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
ECE
I
S
SN
:
208
8-8
7
0
8
Sur
vey:
Model
s
and Pr
ototy
p
es of
Sc
hema
Matchi
ng (Edhy Sutanta)
1
017
B
a
sed on t
y
pe of i
nput
, O
W
L dat
a
m
odel
m
o
st
wi
dely
expl
ore
d
, fol
l
o
wed by
R
D
F, and t
h
en t
h
e
DTD/
XS
D. B
a
sed on appl
i
cat
i
on dom
ai
n, schem
a
m
a
tchi
ng
m
odel
s
evol
ve t
o
wards m
o
re general
i
n
the l
a
st
10-
15
y
ears. It
i
s
supp
ort
e
d
b
y
schem
a
m
a
t
c
hi
ng t
h
e
need t
h
at
i
n
creasi
ngl
y
needed i
n
m
a
ny
appl
i
cat
i
o
ns, so
t
h
at
t
h
e devel
oped m
odel
req
u
i
r
ed t
o
be abl
e
use f
o
r
wi
der
dom
ai
n.
GUI
feat
ure s
u
pp
ort
i
s
al
so gr
owi
n
g o
n
t
h
e
m
odel
s
and pr
ot
ot
y
p
es devel
oped
i
n
t
h
e l
a
st
10-
15 y
ears
.
Of these condi
tions is possible because endorsem
e
nt
a program
m
i
ng language that
m
a
kes it easy developing
appl
i
cat
i
ons based on G
U
I. A
ccordi
n
g t
o
[16
]
, hy
bri
d
m
a
t
c
her com
b
i
n
i
ng
m
u
l
t
i
p
l
e
m
e
t
hods sim
u
lt
aneou
s
l
y
t
o
carry
out
t
h
e
s
c
hem
a
m
a
t
c
hi
ng p
r
ocess,
an
d
supp
osed
t
h
e
u
s
e of
hy
bri
d
m
a
t
c
her wo
ul
d
p
r
ovi
de
bet
t
e
r r
e
sul
t
s
and abl
e
t
o
pro
v
i
d
e im
provem
e
nt
s on perf
or
m
a
nce (effect
iveness) rat
h
er t
h
an i
ndi
vi
d
u
al
m
a
tcher. On t
h
e ot
her
hand
, t
h
e use
o
f
hy
bri
d
m
a
t
c
hing i
s
rarel
y
per
f
orm
e
d by
researchers,
has fo
un
d 9
research
i
n
t
h
e l
a
st
25 y
ears.
Tabl
e
2.
St
at
i
s
t
i
cal
l
y
research,
m
odel
s
, an
d s
c
hem
a
m
a
t
c
hi
ng
pr
ot
ot
y
p
es
i
n
t
h
e l
a
st
25
y
ears
Per
i
od
Nu
m
b
e
r
of
Research/
Publicati-
on
I
nput
Using
M
e
thod
RDF
DT
D/
XS
D
OWL
L
i
ngu-
istic
Based
Struc-
ture
Based
Con-
straint
Based
Instance
Based
M
e
thod
Co
m
b
ination
Auxiliary
Ba
sed
(Dictio-
nar
y
/
Wo
rd
Net
/Cor
pus)
Hy-
br
id
Co
m
po-
site
1989-
1
994
5
1
0
0
0
0
5 1 0
1
0
1995-
1
999
7
4
1
1
4
2
0 1 0
4
4
2000-
2
004
42
18
12
14
20
11
9 16
5
22
17
2005-
2
009
13
5
4
8
10
7
0 6 1
9
7
2010-
1
014
4
4
9
16
20
15
0 11
3
16
12
Total:
71
32
26
39
54
35
14
35
9
52
40
4.
FUTU
RE RE
SEAR
CH
D
I
RECTIO
N
Refers to
classificatio
n
sch
e
ma
m
a
tch
i
n
g
in
[9
],
h
ybrid
m
a
tch
e
r still
o
p
e
n
to
d
e
velo
p
o
n
a
com
b
i
n
at
i
on o
f
t
w
o
or m
o
re
m
e
t
hods o
f
l
i
n
g
u
i
s
t
i
c
-ba
s
ed
,
st
ruct
u
r
e-
base
d, co
nst
r
ai
nt
-
b
ased, i
n
st
a
n
ce-
base
d,
an
d
/
o
r
aux
iliary b
a
sed (th
e
use of d
i
ction
a
ry/Wo
r
dN
et/Co
r
pu
s). Th
is sug
g
e
sts a research
o
ppo
rtun
ities
on
h
ybrid
m
a
tch
e
r.
Devel
opm
ent
of f
u
t
u
re sol
u
t
i
ons i
n
t
h
e sc
hem
a
m
a
t
c
hi
ng can al
so
be
devel
o
pe
d t
o
im
prove t
h
e
co
nv
erg
e
n
ce an
d
reso
lu
tion
on
th
e en
tity ap
p
r
o
a
ch
, su
ch
match
i
n
g
o
n
m
e
tad
a
ta and
in
stan
ce lev
e
l to
i
d
en
tify
th
e sem
a
n
tic r
e
latio
n
s
h
i
p
s
at th
e an
en
tity
o
r
instan
ce [9
]
.
For add
itio
n
a
l in
form
at
io
n
at th
e p
r
esen
t au
tho
r
s
cur
r
ent
l
y
de
ve
l
opi
n
g
a m
odel
and p
r
ot
ot
y
p
e
Hy
bri
d
sche
m
a
m
a
t
c
hi
ng (
n
am
e gi
ven by
[1
6]
,[
19]
)
or
m
i
xed
st
rat
e
gy
(acc
or
di
n
g
t
o
cl
assi
f
i
cat
i
on by
[2
3
]
,[2
8
]
)
by
c
o
m
b
i
n
i
n
g c
o
nst
r
ai
nt
-
b
ased
an
d i
n
st
ance
-ba
s
ed
(i
n a
cl
assi
fi
cat
i
on b
y
[9]
,
[
1
9]
). A hy
b
r
i
d
m
odel
schem
a
m
a
t
c
hi
ng
de
vel
o
pe
d i
n
v
o
l
v
e
s
D
T
M
(dat
a t
y
pe m
a
tcher
)
,
CM
(co
n
strai
n
t m
a
tcher),
a
n
d
IDM
(insta
n
ce
of
data m
a
tcher)
(classified
b
y
[1
8]
).
Ano
t
h
e
r
p
r
ob
le
m
asso
ciated with m
a
tch
i
n
g
sch
e
m
a
is th
e sch
e
m
a
d
e
fin
itio
n d
i
fferen
ces
wh
ich
cause
d by
differences
DBM
S
. Such a cas
e can
be re
so
l
v
ed b
y
u
s
ing
in
term
ed
iary XML langu
age, e.g
.
rel
a
t
i
onal
m
odel
dat
a
base sch
e
m
a
coul
d be
m
a
pped i
n
t
o
X
M
L l
a
ngua
ge u
s
i
ng s-
XM
L w
a
s devel
ope
d b
y
[86]
.
A fram
e
wo
rk
m
a
ppi
n
g
rel
a
t
i
onal
dat
a
base
m
odel
schem
a
i
n
t
o
XM
L l
a
n
gua
ge can
be do
ne i
n
a t
w
o
pha
ses,
i
.
e. m
a
ppi
ng
a
rel
a
t
i
onal
sc
he
m
a
m
odel
s
t
o
UM
L cl
ass
di
a
g
ram
s
, an
d m
a
ppi
ng
UM
L
cl
ass di
a
g
ram
t
o
XM
L
doc
um
ent
[8
7]
.
5.
CO
NCL
USI
O
N
Research on m
odels
and
prot
otypes sc
hem
a
m
a
tching is
still unde
rway a
n
d still ope
n t
o
devel
o
p a
hy
b
r
i
d
m
odel
t
h
at
i
nvol
ves t
w
o
or m
o
re di
ffere
nt
m
e
t
h
o
d
s w
h
i
c
h are
pr
ocesse
d si
m
u
l
t
a
neo
u
s
.
Usa
g
e of
hy
b
r
i
d
m
a
t
c
her w
oul
d p
r
o
v
i
d
e a bet
t
e
r re
sul
t
and i
m
prove
d
per
f
o
r
m
a
nce (e
ffect
i
v
e
n
ess) t
h
an
usi
ng a
n
i
ndi
vi
dual
m
a
tcher
.
Ne
w m
odel
s
an
d
pr
ot
o
t
y
p
e can
be
d
e
vel
o
ped
t
o
e
x
pl
o
r
e sem
a
nt
i
c
rel
a
t
i
ons
hi
ps
at
t
h
e
entities or inst
ance as the
ba
se of m
a
pping t
h
e
relations
hip between sc
he
ma ele
m
ents. Recent resea
r
c
h
by the
aut
h
ors
,
i
s
cur
r
e
nt
l
y
bei
ng
de
vel
o
ped a m
o
d
e
l
and p
r
ot
ot
y
p
i
n
g t
h
at
com
b
i
n
es co
nst
r
ai
nt
-base
d
a
nd i
n
st
ance-
b
a
sed
,
i
n
it invo
lv
es DTM
(d
ata typ
e
m
a
t
c
her),
C
M
(c
o
n
st
r
a
i
n
t
m
a
t
c
her),
and
I
D
M
(i
nst
a
nce
of
dat
a
m
a
tcher
)
.
REFERE
NC
ES
[1]
Cruz I. F.,
et al.
,
“
A
greem
entm
aker:
Effic
i
en
t m
a
tching
for l
a
rge
r
eal
-world
schemas and on
tologies (demo paper)
,”
International Co
nference on Very Large Data B
a
se (
V
LDB)
.
Lyon, France,
vol/issue: 2(2), pp. 1586-1589, 2009.
DOI: 10.14778/1687553.168759
8.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
SN
:
2
088
-87
08
IJEC
E
V
o
l
.
6,
No
. 3,
J
u
ne 2
0
1
6
:
10
1
1
– 10
22
1
018
[2]
He B. and Cha
ng K. C. C., “
S
tatis
ti
cal s
c
h
e
m
a
m
a
tching ac
ros
s
web quer
y
interf
aces
,”
The ACM SIGMOD
International C
onference Man
agement of Da
ta. San Diego
,
California, U
S
A
, pp. 217-2
28, 2003. DOI:
10.1145/872757.872784.
[3]
Do H. H., “Schema matching
and
mapping-based data
integration,”
Ph.D.
T
h
e
s
is,
Interdisciplinar
y
Center for
Bioinformatics
and Depar
t
ment of
Com
puter
S
c
ien
ce,
Uni
v
ers
i
t
y
of
Le
i
p
zig,
Le
ipzig
,
Germ
an
y
.
URL
:
lips.informatik.u
ni-lei
pzig
.de/files/2006-4.pdf.
[4]
Kavitha C
.,
et al.
, “Ontolog
y
based semantic integra
tion of
heterog
e
neous d
a
tab
a
ses,”
Euro
pean Journal o
f
Scien
tifi
c Resear
ch
, vo
l/issue: 64
(1), pp
. 115-122
, 2011.
[5]
Villan
y
i
B.
,
et
al
.
, “A novel fram
e
work for the
com
position of
schem
a
m
a
tcher
s
,”
The
14
th
WS
EAS Internation
a
l
Conference on
Computers, Latest Tre
nds on
Computers. Corfu Island, Greece,
pp. 379-38
4, 2010. URL:
http://dl.acm.o
rg
/citation
.
cfm
?
id
=1981573.19816
41.
[6]
Kim
W
.
and Seo J., “
C
lassif
y
in
g schem
a
tic an
d
data het
e
roge
neit
y in m
u
ltida
t
abase s
y
st
em
s,”
IEEE Journal
,
vol/issue:
vol/iss
u
e: 24(12)
, pp
. 1
2
-18, 1991
. DOI: 10.1109
/2.1168
84.
[7]
Engmann D. and Massmann S., “I
nstance m
a
tch
i
n
g
with COMA
++
,”
Datenbank S
y
steme in Business, Technologie
und Web (
B
TW Workshop)
: M
odel Management and Metada
ta. Aach
en, G
e
rmany,
pp. 28-3
7
, 2007. URL:
http://ceu
r-ws.org/Vol-814/om2011_Tpaper5
.pdf.
[8]
Do H.
H.
,
et al.
, “Comparison of schema ma
tching ev
aluatio
ns,”
The
2
nd
International Wor
k
shop Web and
Databases. In: Lecture Notes I
n
Computer Sc
ience (
L
NCS)
25
93. Springer-Verlag, Germany,
pp. 221-237, 20
03.
URL: http
://
lips.
inform
atik.un
i
-le
i
pzig
.de/f
iles/20
02-28.pdf.
[9]
Be
rnste
i
n P.
A., Ma
dha
va
n J.
,
Rahm
E.
, “
G
eneric
s
c
hem
a
matching
, ten
y
e
ar
s later,”
Ve
ry
L
a
rge
Data Bases
(
V
LDB
)
Endo
wment Journal
, Seattle, Washington, vol/issu
e: 4(11), pp. 695-701, 2011. UR
L:
http://www.vldb
.org/pvldb
/vol4/
p695-bernstein_
madhavan_rahm.pdf.
[10]
Li J
.,
et al.
, “
R
iMOM: A dy
n
a
m
i
c m
u
ltistrateg
y ontolog
y a
lig
nm
ent fram
e
work,”
Journal of IEEE T
r
ansactio
n
Knowledge Data
Engin
eering
, vo
l/issue: 21
(8), pp
. 1218-1232
, 20
09. DOI: 0
.
1109
/TKDE.2008.20
2.
[11]
Berns
t
ein P
.,
et al.,
“The Microsoft reposito
r
y
,”
The
23
rd
International Con
f
eren
ce Very Large Da
tabases (
V
LDB
)
.
Athens
,
Gr
eec
e,
pp. 3-12
, 1997
.
DOI: 10.1.1
.
50.8
527.
[12]
Bernstein P. A., “Apply
i
ng m
odel management to classi
cal metadata prob
lems,”
The
1
st
International Conferen
ce
Innovati
ve Da
ta
Systems Resear
ch (
C
IDR)
. Asilomar, California
,
USA,
pp
. 209-22
0, 2003
. DOI: 10
.1.1
.12.2729
.
[13]
S
t
abenau A.
,
et al.
, “An overview of ensemble,”
Genome Research Journal,
vol/issue: 14(5), pp. 929-933, 2004.
DOI: 10.1101/gr
.1857204.
[14]
Madhavan J.,
et al.
, “Generic schema matching
with Cupid,”
Th
e
2
7
th
International Conference on Very Large
Data Bases (
V
L
D
B)
. Roma, I
t
al
y,
pp
. 49-58
, 200
1. URL:
ht
tp://d
l
.
acm
.org
/ci
t
at
ion
.
cfm
?
id=645927
.672191.
[15]
Ma
ssma
nn S.
,
e
t
al., “
Evolu
tio
n of the COMA match sy
stem,”
The
6
th
International Workshop on Ontology
Matching
(
O
M-2
011)
. Bonn, G
e
rmany,
pp
. 49-60
, 2011. URL: h
t
t
p
://ceur-ws.org/
Vol-814/om
2011_Tpaper5
.pdf.
[16]
Rahm E. and Bernstein P. A., “
A
survey
of
app
r
oaches
to au
to
m
a
tic s
c
hem
a
m
a
tch
i
ng,”
Very Large
Database
s
(
V
LDB
)
Journal
, vol/issue: 10(4)
, pp. 334-350, 20
01. DOI: 10
.100
7/s007780100057.
[17]
Shvaiko P. and Euzen
at J., “A survey
of
schema-based matching
approaches
,”
Jo
urnal on Data S
e
mantics,
vol. 4
,
pp. 146-171
, 20
05. DOI: 10
.100
7/11603412_5.
[18]
Karasneh Y.,
et al.
, “Integr
a
tin
g schemas of heterogen
e
ous re
lational databases
thr
ough sch
e
ma match
i
ng,”
The
11
th
Internationa
l Conference on
Inform
ation Integration and W
e
b
-
based Applic
ations and Services
(
iiWAS)
.
Kuala
Lumpur, Malaysia,
pp
. 209-216
,
2009. DOI: 10
.1
145/1806338.18
06380.
[19]
Özsu M. T. and
Valduriez P. P., “Princ
iple
s of distribute
d
da
ta
base
s
y
ste
m
s,
”
Pearson Education
,
Inc.,
Springer
,
New Y
o
rk,
US
A,
2011. DOI: 10
.1
007/978-1-4419-
8834-8.
[20]
Milo T. and Zoh
a
r S., “Using
schema matching
to sim
p
lif
y
heter
ogeneous data tr
anslation
,
”
The
24
th
Internationa
l
Confe
r
e
n
ce
on Ve
ry
Large
Data Base
s
(VL
D
B).
Ne
w Y
o
rk,
USA,
pp. 122-133, 1998. URL:
http://www.vldb
.org/conf
/1998/p
122.pdf.
[21]
Melnik S.,
et a
l
.
,
“RONDO: A
programming
platform fo
r generic model management,”
The ACM-SIGMOD
Conference on Management of Data (
S
I
G
M
O
D)
. San Diego, California
, USA,
pp. 193-204, 2003. DOI:
10.1145/872757.872782.
[22]
Banek M.,
et al.
, “Automated integration of hete
rogen
e
ous data wa
rehouse schemas,”
Interna
tional Journal o
f
Data Warehousing and Min
i
ng (
I
JDWM)
, vol/issu
e: 4(4)
, pp
. 1-21
, 2008. DOI: 10.4
018/jdwm.2008100101.
[23]
Gross A.
,
et al.
, “On
matching
large life scie
nce ontologies in parallel,”
The
7
th
International
Conference Data
Integration
in th
e Life S
c
i
e
n
ces
(
D
IL
S)
. Gothen
bur
g, Sweden
,
p
p
. 35-49, 2010
.
DOI: 10.1007/9
78-3-642-15120-
0_4.
[24]
Bergam
as
chi S
.
,
et al.
, “
S
em
ant
i
c in
tegra
tion of
s
e
m
i
s
t
ruct
ured
and structur
ed d
a
ta sources,”
SIGMOD Record
,
vol/issue:
28(1),
pp. 54-59
, 1999
. DOI: 10.1145
/3
09844.309897.
[25]
Li W. S. and Clifton C., “Semint: A tool for identif
y
ing
attribute correspondences in he
terogen
e
ous datab
a
ses using
neural network,”
Data and Knowledge En
gineering Jour
nal,
vol/issue: 33(1), pp. 4
9
-84, 2000. DOI:
10.1016/S0169-023X(99)00044-0.
[26]
Doan A.
H.,
et
al
., “Reconciling sc
hem
a
s
of
dis
p
arat
e d
a
ta
s
ources
-a m
a
chin
e-le
arning
appro
ach,
”
The A
C
M
SIGMOD Intern
ational Conference Managemen
t
of Data
. Santa Barbar
a, Calif
orni
a, USA, pp. 509-520, 2001.
DOI: 10.1145/3
76284.375731.
[27]
Li W
.
S and Clifton C.
, “
S
emanti
c integr
ation
in
heterogeneo
u
s databases us
ing neural netw
orks,”
The
20
th
International C
onference on Very Large Da
ta
Bases
(
V
LDB
)
. Santiago de Chile, Chile,
pp. 1-12, 1994. URL:
https://www.cerias.purdue
.edu/assets/pdf/bibtex_
archiv
e/2001-86
-report.pdf
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
ECE
I
S
SN
:
208
8-8
7
0
8
Sur
vey:
Model
s
and Pr
ototy
p
es of
Sc
hema
Matchi
ng (Edhy Sutanta)
1
019
[28]
Rahm E., “Tow
ards large-scale
sche
ma and ontolog
y
matching
,” in Bell
ahsene
Z, Bonif
a
ti A,
Rahm E.,
S
c
hema
matching and mapping, data-centr
ic systems and applications
. Springer. New York, US
A, pp. 3-28, 2011. DOI:
10.1007/978-3-6
42-16518-4_1.
[29]
Bellahs
ene Z
.,
et al.
, “Schema match
i
ng and map
p
ing, data-
centr
i
c
s
y
s
t
em
s
and ap
plic
ations
,
”
Springer,
New York,
USA, 2011. DOI: 10.1007
/978-3-
642-16518-4.
[30]
Ba
rra
sa
J.,
et al.
, “R2O, An extensible and
semantically
b
a
sed d
a
tab
a
se to on
tolog
y
mapping
language,”
The
2
nd
Workshop on
Semantic Web
and Databas
es
(
S
WDB2004)
.
Toronto, Can
ada,
pp. 92-11
9, 2004. URL:
http://www.cs.man.ac.uk
/~ocorcho/documents/SWD
B2004_BarrasaEtAl.pdf.
[31]
Evermann J., “An explorator
y
stud
y
of
datab
a
se integra
tion p
r
ocesses,”
IEEE Tran
sactions on Knowledge and
Data Engin
eerin
g
Journal
, vol/is
sue: 20(1), pp
. 9
9
-115, 2008
. DOI: 10.1109
/TKDE.2007.190675
.
[32]
Bizer C.,
et al.
, “Linked data-the stor
y
so far,”
International
Journal of Seman
tic Web Infor
m
ations Systems
,
vol/issue:
5(3), p
p
. 1-22
, 2009
. D
O
I: 10.4018/jswis.2009081901.
[33]
Parundekar R
.
,
et a
l
.
, “Link
i
ng
and bu
ilding
o
n
tologies
of lin
k
ed data,”
The
9
th
International Semantic W
e
b
Conference (
I
SWC
)
.
Shanghai, China,
pp. 598-614, 2010. URL:
http://www.
isi.
edu/in
tegration/papers/par
undekar10-iswc.
pdf.
[34]
Falconer S. M. and Noy
N. F., “I
nter
activ
e techniques to support
ontolog
y
matc
hing,” in Bellahsene Z, Bonifati A,
Rahm
E.,
S
c
hem
a
matching and
mapping, data-
centric systems and applications
, Springer.
New
York,
USA,
pp.
29-52, 2011
. DOI: 10.1007
/978-3
-
642-16518-4_2.
[35]
Aum
u
eller D.,
et al.
, “Schema and ontolog
y
matching with CO
MA++,”
The SI
GMOD
(
d
emo p
aper)
, Baltim
ore
,
Mar
y
land, USA, pp. 906-908, 20
05. DOI: 10
.114
5/1066157.1066
283.
[36]
Seligm
a
n L.
,
et al.
, “OpenII: An open source inf
o
rmation integr
ation toolkit,”
The ACM SIGMOD International
Conference on
Management of Data. I
ndianapolis,
I
ndiana, USA,
pp. 1057-10
60, 2010. DOI:
10.1145/180716
7.1807285.
[37]
Euzen
at J
.
,
et
al
.
, “Results of the ontolog
y
ali
gnm
ent evalu
a
ti
on initi
at
ive 20
10,”
The
5
th
IS
WC Workshop on
Ontology Ma
tching (
O
M
)
. Shanghai, Ch
ina,
2010
. URL:
htt
p
://d
isi.unitn
.i
t/~p2p/O
M
-2010/oaei10_
paper0.pdf
.
[38]
Hernández M. A
.,
et a
l
.
,
“CLIO:
A semi-automatic
tool for sch
e
ma mapping (s
oftware demonstration),”
The
A
C
M
SIGMOD International Conference Managemen
t
of
Data. San
ta
Barbara, California, USA,
pp
. 607, 2001. DOI:
10.1145/376284.375767.
[39]
Naumann F.,
et al.
, “
A
ttribu
t
e
cl
as
s
i
fica
tion us
in
g featur
e ana
l
y
s
i
s
,”
Poster. The
18th Internation
a
l Conference o
n
Data Engineering (
I
C
D
E)
.
San Jose, California,
USA,
pp. 271, 2002. URL: www.hpi.u
ni-
potsdam.de/fileadmin/hpi/FG_Nauma
nn/publications/ICDE02Poster.pdf
.
[40]
Popa L.,
et al.
,
“Mapping XML
and relation
a
l schemas w
ith CL
IO (software
demonstration),”
T
h
e Internationa
l
Conference on
Data Enginnering (
I
CDE
)
. San Jose, California, US
A, pp. 498-4
99, 2002. URL:
http:/
/disi.un
itn
.i
t/~velg
ias/docs/
PopaHVMNH02.pdf.
[41]
Haas
L. M
.,
et a
l
.
, “CLIO grows up: from resear
ch prototy
p
e to
industrial
tool,”
The ACM SIGM
OD Internationa
l
Conference Man
agement o
f
Data
. Ba
ltimore, Mar
y
land, USA,
pp.
805-810, 2005
.
DOI: 10.1145/1
066157.1066252
.
[42]
Kang J. and Nau
ghton J., “On sc
hema matching
with opaque colu
mn names and data v
a
lu
es,”
Th
e ACM SIGMO
D
International C
onference Man
agement of Da
ta. San Diego
,
California, U
S
A,
pp. 205-2
16, 2003. DOI:
10.1145/872757.872783.
[43]
Chien B. C.
and
He S. Y., “A h
y
b
rid
a
pproach f
o
r automatic schema matching,”
The
9
th
International Conference
on Machin
e Learning
and Cybe
rnetics. Qingdao
, China,
pp
. 2881-2886, 2010. DOI:
10.1109/ICMLC
.
2010.5580776
.
[44]
Li W
.
S
.,
et al.
, “Database integration using n
e
ural ne
tworks: i
m
p
lem
e
ntati
on and
experiences,”
Knowledge and
Information Systems Journal
, vol/issue: 2(1)
, pp
.
73-96, 2000
. DOI: 10.1007
/s101150050004.
[45]
Do H.
H.
and
Rahm E.,
“COMA: A s
y
stem
for flex
ible
com
b
ination
of sch
e
m
a
m
a
tching
ap
proach,
”
The 28
th
Conference on Very Large Data Bases
(
V
LDB
)
. Hong
Kong,
China,
pp. 610-621, 2002. URL: http://dbs.uni-
leip
zig.d
e
/fi
l
e
/
C
O
MA.pdf.
[46]
Madhavan J.,
et al.
, “Corpus-Based Schema Matching,”
The IJCAI-03 Workshop o
n
Information Integration on the
Web (
IIWeb)
. Acapulco
, Mex
i
co
,
pp. 59-63
, 2003
. DOI: 10.1109
/I
CDE.2005.39
.
[47]
Dham
ankar R.,
et al.
, “IMAP:
discovering co
mplex semantic
matches between da
ta
ba
se
sc
he
ma
s,
”
The
A
C
M
SIGMOD International Conference Management of Data
. Paris, France, pp. 383-394
, 2004. DOI:
10.1145/100756
8.1007612.
[48]
Berns
t
ein P
.
A
.,
et al.
, “Industrial-strength
sch
e
ma matching
,”
ACM SIGMOD Record
, vol/issue: 3
3
(4), pp. 38-53
,
2004. DOI: 10
.1
145/1041410.10
41417.
[49]
Dragut E. and
Lawrence R
.
, “Co
m
posing
mappings
between schemas using a referen
c
e ontolog
y
,
”
The
International Co
nference on Ontologies
, Databa
ses, and Applica
tions of Se
mantics (
O
DBASE)
. Larnaca, Cyprus,
pp. 783-800
, 20
04. DOI: 10
.100
7/978-3-540-304
68-5_50.
[50]
Mork P. and Bernstein P. A., “Adapting a g
e
neric ma
tch algor
ith
m to align ontologies of human anatom
y
,
”
The
20
th
International C
onference on Data Engine
ering
(
I
CDE
)
. Boston, Massachusetts, USA,
pp. 787
-790, 2004. DOI:
10.1109/ICDE.2
004.1320047.
[51]
Tu K. W
.
and Y
u
Y., “
C
MC: co
m
b
ining m
u
ltipl
e
schem
a
-m
at
chi
ng strateg
i
es b
a
sed on cr
edib
ili
t
y
predi
c
tion
,
”
The
10
th
Internationa
l Conference on
Database Systems for
Advanced
Applica
tions (
D
ASFAA)
. Beijing
,
China,
pp
. 888-
893, 2005
. DOI:
10.1007/114080
79_80.
[52]
J
i
an N
.,
et al.
, “
F
alcon-AO: Alig
ning ontologies with Falcon,”
The K-CAP Workshop on Integrating Ontologies (K-
CAP’05)
. Ban
ff,
Canada,
pp. 85-
91, 2005
. DOI: 1
0
.1016/j.websem
.2008.02.006.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
SN
:
2
088
-87
08
IJEC
E
V
o
l
.
6,
No
. 3,
J
u
ne 2
0
1
6
:
10
1
1
– 10
22
1
020
[53]
J
ean-M
ar
y
Y. R.
,
et al.
, “Ontolog
y
matching with
semantic verif
i
cation,”
Web Semantics Journal,
vol/issue: 7(3),
pp. 235-251
, 20
09. DOI: 10
.
101
6/j.websem.200
9.04.001
.
[54]
Palopoli L.
,
et a
l
.
,
“
S
em
i-autom
a
ti
c, s
e
m
a
ntic
di
scover
y
of p
r
op
erties from database schemes,”
The Internat
ion
a
l
Database Engineering and Applications Symposium
(
I
DEAS)
. Ca
rdiff, Wales, Unit
ed Kingdom, pp. 244-253, 1998.
DOI: 10.1109/I
D
EAS.1998.694384.
[55]
Palopoli L.
,
et a
l
.
, “A unified
gr
aph-based f
r
amework for der
i
vin
g
nom
inal
inters
chem
e prop
erti
e
s
, t
y
pe
conf
lic
ts
and object cluster similarities,”
The
4
th
IFCIS International
Conference
on Cooperative Information Systems
(
C
oopIS
)
.Edinburgh, Scotland,
p
p
. 34-35
, 1999
.
DOI: 10.1109/C
OOPIS.1999.792152.
[56]
Palopoli L.
,
et a
l
.
, “The s
y
stem Dike: Towards th
e semi-automatic s
y
nth
e
sis of
cooperative infor
m
ation s
y
stems
and data wareh
ouses,”
The Ch
alenges: 2000
ADBIS-DASFAA Sym
posium on Advances in
Databases and
Information Sy
ste
m
s,
Enlarge
d
4
th
East-European
Conferen
ce on
Advances
in Databases and Information Systems.
Prague, Czech
Republic,
pp. 108-
117, 2000
. URL: http://dblp
.
uni-
t
ri
er.d
e/r
ec/b
i
b/conf/adbis/Palopo
liTU00.
[57]
Castano S. and
Antonellis V. D., “
A
schem
a
anal
ysis
and reconc
ili
ation to
ol environm
ent
for heterog
e
neo
u
s
da
ta
ba
se
s,
”
The
International Database Engin
eering and Appl
ications Symposium (IDEAS)
.
Montreal, Que,
pp. 53-
62, 1999
. DOI: 1
0
.1109/IDEAS.1
999.787251.
[58]
Bergam
as
chi S
.
,
et al.
, “
S
em
antic int
e
gra
tion
of hete
rogen
e
ous information sources,”
Data
and Knowledge
Engineering
Jou
r
nal,
vol/issue: 3
6
(3), pp
. 215-24
9, 2001
. DOI: 10
.1016/S0169-023X(00)00047-1.
[59]
Mitra P.,
et a
l
., “Semi-automatic
integr
a
tion
of knowledge s
ources,”
The 2
nd Internationa
l Conferen
ce o
n
Information Fusion (
F
USION
)
. Sunnyvale, Ca
lifor
n
ia, USA,
1999. URL:
https://www.researchgate.net/pub
lication/263
0475
_ Semi-automatic
_Integr
a
tion_of
_Knowledge_Sources.
[60]
Mitra P.
,
et al.
, “Graph-oriented model for ar
ticula
tion of
ontolog
y
interdep
endencies,”
The 7
th
International
Conference Extending Database Tec
hnology (
E
DBT)
. Konstanz,
Germany,
pp. 86-100, 2000. DOI: 10.1007/3-540-
46439-5_6.
[61]
Mitra P. and Wiederhold G., “Resolving term
in
ological heterog
e
ne
ity
in
ontolo
g
ies,”
The EC
AI
-02 Workshop o
n
Ontologies and Semantic Int
e
roperabilit
y, Euro
pean Conf
erence on Artifi
cial I
n
tell
igen
ce (
E
CAI)
. Lyon, France,
pp. 45-50
, 2002
. DOI: 10.1145
/5
05168.505196.
[62]
Giunchiglia F.,
et a
l
.
, “A larg
e
scale taxonom
y
mapping
evaluation,”
The
4
th
International Conference Semantic
Web Conference (
I
SWC)
. Galway, Ir
eland,
pp. 6
7
-81, 2005
. DOI: 10.1007
/11574
620_8.
[63]
Palopoli L
.,
et al.
, “Uniform techniques for derivi
ng similarities of objects and
subschemes in
heterog
e
neou
s
da
ta
ba
se
s,
”
IEEE Transaction Knowledge and
Data Engineering,
vol/issue:
15(2), pp. 271-294, 2003. DOI:
10.1109/TKDE.2003.1185834.
[64]
He H.
,
et al.
,
“
W
i
s
e
-Integrato
r
-
A
n autom
a
tic
i
n
tegra
t
or of we
b s
earch
interf
a
ces
for e-
com
m
erce
,”
The 29th
International C
onference on Very Larg
e Data Bases (
V
LDB
)
. Berlin, Germany,
pp. 357-3
68, 2003. URL:
www.vldb.org/conf/2003/
pap
e
rs/S12P01.pdf.
[65]
He B.
,
et al.
, “
D
iscovering
co
mplex matching
s across web qu
er
y
interfaces-a
correla
tion
mining approach,”
The
10
th
ACM SIGKDD International Conferen
ce Kn
owledge Disc
o
v
ery and Data M
i
ning. S
e
attle, W
a
shington, USA,
pp. 148-157
, 20
04.
DOI:
10.114
5/1014052.1014
071.
[66]
Noy
N. F.
a
n
d Muse
n M.
A., “The
Prom
pt
Suite: In
ter
act
iv
e tools
for ontolog
y
merging
and mapping,”
International Journal of
Human-Computer Studies
, vol/issue: 59(6), pp. 983-102
4, 2003. DO
I:
10.1016/j.ijh
cs.2
003.08.002
.
[67]
Noy
N. F.
a
nd
Muse
n M.
A.
, “Using
prompt ontolog
y
-
comparison tools in
the EON
ontolog
y
alignment contest,”
The
3
rd
International Workshop Evaluation of O
n
to
logy-
B
ased Tools (
E
ON
)
.
Hiroshima, Japan,
pp. 79-90, 2004
.
DOI: 10.1.1
.
91.1
763.
[68]
Euzen
at J
.,
et al.
, “Ontolog
y
alig
nment with OLA,”
The 3rd International Workshop
Evaluation
of Ontology Based
Tools (
E
ON
)
.
Hiroshima, Japan,
pp. 56-88
, 2004
. URL:
ceur-ws.o
r
g/Vol-128/EON
2004_EXP_Euzenat.pdf
[69]
Ehrig M. and Staab S., “QOM-quick ontolog
y
mapping,”
The
3
rd
International S
e
mantic Web Con
f
erence (
I
SWC)
.
2004, Hiroshima, Japan
,
pp
. 683
-
697, 2004
. DOI:
10.1007/978-3-5
40-30475-3_47.
[70]
Ehrig M.
and Su
re Y., “Ont
ology
alignment-K
a
r
l
sruhe,”
The 3rd
Internationa
l W
o
rkshop Evaluation of Onto
logy-
Based Tools (
E
ON
)
Hiroshima, Japan,
pp. 48
-55, 2004. URL: sunsite.informatik
.r
wth
-
aach
en.d
e/Publi
c
ations/CEUR-W
S
/Vol-128/EON2004_EXP_Ehrig.pdf.
[71]
Giunchiglia F.,
et al.
, “
S
-M
atch: an algori
t
hm
and an im
pl
ementation of semantic matching
,”
Th
e
1
st
European
Semantic W
e
b Symposium (
E
SW
S)
. Her
a
klion, C
r
ete, Gr
ee
ce
,
pp
. 61-75, 2004.
DOI: 10.1007/978-3-540-25956-
5_5.
[72]
Giunchiglia F.,
et al.
,
“E
ffi
c
ie
nt
se
ma
nt
i
c
ma
tchi
ng,
”
The
2
nd
European Sema
ntic W
e
b Conference (
E
SWC)
.
Her
a
klion,
Cr
et
e
,
Gr
ee
ce
,
pp
. 27
2-289, 2005
. DOI: 10.1007
/1143
1053_19.
[73]
Benkley
S
.
,
et al.
,
“
D
ata
Elem
en
t
Tool-B
as
ed An
al
y
s
is
(DEL
TA),
”
MITRE Techn
i
cal
Report MTR’
95 B147
, 1995.
[74]
Clifton C.,
et al.
, “
E
xperi
ence
with a com
b
ined approach to at
tr
ibute-matching
across he
terogeneous databases,”
The
7
th
IFIP
2.6 Working C
onf. on Databa
se Semantics.
Leysin, Switzerland,
pp. 428-4
51, 1997. DOI:
10.1007/978-0-3
87-35300-5_18.
[75]
Melnik S.,
et al.
, “Similarity
Flo
oding: A vers
atile graph matching algorithm,”
The
18
th
International Conference
on Data Eng
i
neering (
I
CDE)
. San Jose, California, USA,
pp
. 117-
128, 2002
. DOI:
10.1109/ICDE.2
002.994702.
[76]
Lee M
.
L
.,
et a
l
.
, “XClust: Clustering XML schema
s for effectiv
e integration,”
Th
e
1
1
th
International Conferen
ce
on Information a
nd Knowledg
e
Management (
C
IKM’02)
. Virginia, USA,
pp
. 292-
299, 2002
. DOI:
10.1.1
.
62.1532
.
[77]
Lu J
.,
et al.
, “An experiment on
the matchi
ng
and reuse of XML schemas,”
The
5
th
Internation
a
l Conference on
Web Eng
i
neerin
g (
I
CWE)
. Sydney,
Australia
,
pp
.
273-284, 2005
.
DOI: 10.1007/1
1531371_38.
Evaluation Warning : The document was created with Spire.PDF for Python.