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1
ON
TOLO
GY
-DRI
VE
N W
EB
SERV
ICE
S CO
MPO
SITI
ON
TE
CHN
IQU
ES RU
OYA
N Z
HAN
G
Advi
sor:
Dr.
I. Bu
dak
Arpi
nar
Com
mitt
ee:
Dr.
Ham
id A
rabi
naD
r. Am
it Sh
eth
2
Pres
enta
tion
Layo
ut
Bac
kgro
und
on W
eb S
ervi
ces
Cha
lleng
es fo
r Web
Ser
vice
s C
ompo
sitio
nIn
terfa
ce-M
atch
ing
Aut
omat
ic (I
MA
) C
ompo
sitio
nH
uman
-Ass
iste
d (H
A) C
ompo
sitio
nC
oncl
usio
ns a
nd F
utur
e W
ork
3
Web
Ser
vice
•A
Web
Ser
vice
is a
sof
twar
e ap
plic
atio
nid
entif
ied
by a
UR
I, w
hose
in
terfa
ces
and
bind
ing
are
capa
ble
of b
eing
def
ined
, des
crib
ed a
nd
disc
over
ed b
y X
ML
artif
acts
and
supp
orts
dire
ct in
tera
ctio
ns w
ith
othe
r sof
twar
e ap
plic
atio
ns u
sing
XM
L ba
sed
mes
sage
s vi
a In
tern
et-b
ased
pro
toco
ls(W
3C d
efin
ition
).•
A s
elf-c
onta
ined
, sel
f-des
crib
ed, a
nd s
elf-a
dver
tised
com
posi
tion
unit
(app
licat
ion/
com
pone
nt).
Serv
ice P
ub
lica
tio
n/
Dis
covery
UD
DI
Serv
ice D
esc
rip
tio
n W
SD
L
XM
L M
ess
ag
ing
SO
AP
Tra
nsp
ort
Netw
ork
HTTP
Web
Ser
vice
s St
ack
4
How
they
wor
k an
d w
hen
we
need
them
?
Bin
d
Ser
vice
Reg
istry
/U
DD
I
Cus
tom
erS
ervi
ce P
rovi
der
WS
DL
Soa
p M
essa
ge
Send
Req
uest
(XM
L)
Publ
ish
WS
Rec
eive
For
mC
heck
Pos
tcod
e
Com
pany
AC
ompa
ny B
5
Web
is tu
rnin
g in
to a
col
lect
ion
of W
eb
Serv
ices
•The
num
ber o
f com
pani
es th
at
have
com
plet
ed a
n IT
pro
ject
in
volv
ing
Web
ser
vice
s st
anda
rds
has
grow
nin
a s
urve
y re
leas
ed in
20
03 [T
echW
eb].
•The
per
cent
age
of s
urve
yed
firm
s us
ing
stan
dard
s su
ch a
s X
ML
or
SO
AP
incr
ease
d fro
m 1
1pe
rcen
t in
mid
-200
2 to
31
perc
ent 2
003,
m
arke
t res
earc
her F
orre
ster
R
esea
rch
Inc.
6
Wor
kflo
ws
Glo
bal
Enterprise
Inte
r-En
terp
rise
Web
Pro
cess
es
(Com
posi
tion)
E-Se
rvic
es
Glo
baliz
atio
n of
Web
pro
cess
es
Dis
tribu
ted
Wor
kflo
ws
B2B
[Ref
: She
th, C
ardo
so W
STut
oria
l]
Ente
rpris
e
7
App
licat
ion
inte
grat
ion/
Web
serv
ice
com
posit
ion
An
Info
wor
ldsu
rvey
sho
ws
that
app
licat
ion
inte
grat
ion
cost
s ar
e at
leas
t 25%
of th
e to
tal I
T bu
dget
at m
any
com
pani
es.
Gar
tner
Dat
aque
st p
redi
cts
spen
ding
on
inte
grat
ion
proj
ects
will
reac
h a
stag
gerin
g $1
0.6
billi
on in
200
6 .
The
sam
e su
rvey
indi
cate
d th
at 5
5%of
the
IT m
anag
ers
polle
d sa
id W
eb s
ervi
ces
will
mak
e in
tegr
atio
n pr
ojec
ts
mor
e vi
able
.W
hy W
eb S
ervi
ce?
Ope
n st
anda
rds
Wid
espr
ead
supp
ort
and
univ
ersa
l acc
ess
Pla
tform
-neu
tral
(Hop
eful
ly) 0
-line
app
licat
ion
deve
lopm
ent (
i.e.,
auto
mat
ical
ly c
ompo
sed
Web
Pro
cess
)
8
Com
posit
ion
idea
sPa
st/ M
anua
lN
ow/S
emi-A
utom
atic
Futu
re/A
utom
atic
sS
SSS
SS
Web
Ser
vice
O
nto
log
y
sS
SSS
SS
Web
Ser
vice
On
tolo
gy
SS
SSS
SSS
S
Sem
antic
Func
tiona
lity
sS
SS
S
9
Com
posit
ion
Chall
enge
sH
eter
ogen
eity
and
Aut
onom
yS
ynta
ctic
, sem
antic
and
pra
gmat
icC
ompl
ex ru
les/
regu
latio
ns re
late
d to
B2B
and
e-
com
mer
ce in
tera
ctio
nsS
olut
ion:
Mac
hine
pro
cess
able
desc
riptio
ns
Dyn
amic
natu
re o
f bus
ines
s in
tera
ctio
nsD
eman
ds: E
ffici
ent D
isco
very
, Com
posi
tion,
etc
.
Scal
abili
ty(E
nter
pris
es →
Web
)N
eeds
: Aut
omat
ed s
ervi
ce d
isco
very
/sel
ectio
n an
d co
mpo
sitio
n
Sem
antic
sis t
he m
ost i
mpo
rtant
ena
bler
to
addr
ess t
hese
cha
lleng
es[R
ef: S
heth
, Car
doso
WS
Tut
oria
l]
10
Mor
e ch
allen
ges
Cha
lleng
es o
fca
ptur
ing
rela
tions
am
ong
serv
ices
sem
antic
ally
(e
.g.,
inte
rface
mat
chin
g, c
ompl
imen
tary
func
tion
etc.
),m
odel
ing
func
tiona
litie
s se
man
tical
ly,
deve
lopi
ng e
ffici
ent f
ilter
ing
mec
hani
sms
base
d on
use
r pre
fere
nces
/con
text
,fin
ding
an
optim
al c
ompo
sitio
n am
ong
alte
rnat
ives
th
roug
h qu
ality
met
rics.
11
Web
Ser
vice
Com
posit
ion:
Indu
stry
WS
DL
+ B
PE
L4W
SIn
terfa
ce d
escr
iptio
n in
WS
DL
BP
EL4
WS
spec
ifies
the
role
s of
eac
h of
the
partn
ers
and
the
logi
cal f
low
of m
essa
ge (s
eque
nce,
sw
itch,
w
hile
).E
rror
han
dlin
g an
d m
essa
ge c
orre
latio
n.Th
ey d
on’t
tell
muc
h fo
r gen
erat
ing
a co
mpo
sitio
n ye
t th
ey c
an m
odel
a c
ompo
sitio
n in
term
s its
dat
a an
d co
ntro
l flo
w.
12
Web
Ser
vice
Com
posit
ion:
Sem
antic
-Web
Co
mm
unity
ME
TEO
R -S
Sem
antic
ann
otat
ion,
dis
cove
ry, c
ompo
sitio
n of
WS
sD
ata,
Ope
ratio
nal,
Func
tiona
l, an
d Q
oS S
eman
tics
Gol
og (A
I Pla
nnin
g)A
met
hod
is p
rese
nted
to c
ompo
se W
eb S
ervi
ces
by a
pply
ing
logi
cal i
nfer
enci
ng te
chni
ques
on
pre-
defin
ed p
lan
tem
plat
es
[McI
iraith
& S
on, 2
002]
.S
eman
tic/o
ntol
ogic
al re
pres
enta
tion
of s
tate
s, a
ctio
ns, g
oals
, an
d ev
ents
are
nee
ded.
H
ow to
spe
cify
pre
-and
pos
t-con
ditio
ns in
an
expl
icit
way
by
refe
rring
to s
truct
ural
pro
perti
es o
f inc
omin
g an
d ou
tgoi
ng
mes
sage
s an
d in
tern
al s
tate
of t
he B
PE
L4W
S p
roce
ss
[Srit
asta
va03
].
13
Aut
omat
ic Co
mpo
sitio
n
Seaf
ood
Mat
chin
g W
ine
Pric
e ?
14
Aut
omat
ic Co
mpo
sitio
n Te
chni
que
No
pred
efin
ed c
ompo
sitio
n te
mpl
ate
Web
ser
vice
s ar
e as
sem
bled
thro
ugh
a fo
rwar
d -
chai
ning
met
hod
Inte
rface
rela
tions
(i.e
., m
atch
ing)
with
diff
eren
t w
eigh
ts a
re c
ompu
ted
amon
g W
S in
terfa
ces
as w
ell
as u
ser I
/Os
and
WS
inte
rface
s.O
ntol
ogic
al m
easu
res
are
used
for m
atch
ing
A W
S n
et is
gen
erat
ed fo
r fin
ding
an
optim
al p
ath
amon
g va
rious
com
posi
tions
Ada
pted
Bel
lman
-For
d S
horte
stpa
th (d
ynam
ic
prog
ram
min
g) a
lgor
ithm
. ( M
ultip
le in
puts
and
out
puts
)E
xplo
ited
DA
ML-
S W
S d
escr
iptio
ns
15
Web
Ser
vice
Mod
eling
: DA
ML-
S
Ser
vice
Ser
vice
Pro
file
Ser
vice
Gro
undi
ng
Ser
vice
Mod
elW
hat t
he s
ervi
ce
does
How
it w
orks
How
to a
cces
s it
Des
crip
tion
Func
tiona
lity
Func
tiona
l Attr
ibut
es
16
Win
e-Se
arch
Ser
vice
in D
AM
L-S
<pro
fileH
iera
rchy
:Win
e-S
earc
h rd
f: ID
="Pr
ofile
-Win
e-Se
arch
er">
<ser
vice
: pre
sent
edB
yrd
f: re
sour
ce="
win
e-se
arch
er.o
wl#
win
e-se
arch
er" /
> <p
rofil
e: h
as_p
roce
ss rd
f: re
sour
ce="
win
e-se
arch
er-P
roce
ss. o
wl#
Win
e-S
earc
her
Pro
cess
Mod
el"/>
<pro
file:
ser
vice
Nam
e> W
ine-
Sea
rche
r.com
</pr
ofile
: ser
vice
Nam
e><p
rofil
e:te
xtD
escr
iptio
n>W
ine-
Sea
rche
r hel
ps …
.. da
taba
se <
/pro
file:
text
Des
crip
tion>
<pro
file:
qua
lityR
atin
g><p
rofil
e: q
ualit
yRat
ing
rdf:I
D="
win
e-se
arch
-Rat
ing"
><p
rofil
e: ra
tingN
ame>
very
good
</pr
ofile
: rat
ingN
ame>
<pro
file:
ratin
grd
f:res
ourc
e="o
wl-s
/1.0
/Con
cept
s.ow
l#G
oodR
atin
g">
</pr
ofile
: Qua
lityR
atin
g>
<pro
file:
has
Inpu
trd
f:res
ourc
e="S
ervi
ce-C
once
pt.o
wl#
win
eNam
e" />
<p
rofil
e: h
asO
utpu
trdf
:reso
urce
="S
ervi
ce-C
once
pt. o
wl#
win
ePric
e" />
</pr
ofile
Hie
rarc
hy:W
ine-
Sea
rch
Ser
vice
>
17
Web
Ser
vice
Ont
olog
y an
d D
omain
Ont
olog
yAl
coho
l
Beer
Win
eSp
orts
Whi
teRe
dBl
ush/
Bub
bly
Win
e N
ame
Vint
age
Mer
chan
t Loc
atio
n
Pric
e
Prop
ertie
s
Sea
rchi
ng S
ervi
ce
Mat
chin
g S
ervi
ceSe
arch
ing
Serv
ice
Men
/W
omen
Dre
sses
Win
e/Fo
odBo
ok
Win
e
Win
e-Se
arch
er(D
AM
L-S
Win
e-Se
arch
er(D
AM
L-S
I/O R
efer
ence
to
18
Que
ry F
orm
at
<Que
ry: Q
uery
Nam
e>W
ine
Pric
e</p
rofil
e: s
ervi
ceN
ame>
<Que
ry: q
ualit
yRat
ing>
<pro
file:
qua
lityR
atin
grd
f:ID
="Q
uery
-Rat
ing"
><p
rofil
e: ra
tingN
ame>
aver
age
</Q
uery
: rat
ingN
ame>
<pro
file:
ratin
grd
f:res
ourc
e="o
wl-q
/1.0
/Con
cept
s.ow
l#G
oodR
atin
g">
</Q
uery
:Qua
lityR
atin
g>
<Que
ry: h
asIn
put
rdf:r
esou
rce=
"Ser
vice
-Con
cept
.ow
l # s
eafo
od/F
ood"
/>
<Que
ry: h
asO
utpu
trd
f:res
ourc
e="S
ervi
ceC
once
pt.o
wl #
win
eNam
e/W
ine"
/>
<Que
ry: h
asO
utpu
trd
f:res
ourc
e="S
ervi
ce-C
once
pt.o
wl #
win
ePric
e/W
ine
<dam
l:Res
trict
ion
dam
l:onP
rope
rty rd
f:res
ourc
e=Fr
anc"
></
dam
l:Res
trict
ion>
19
Web
Ser
vice
Net
wor
k
Food
-Win
e M
atch
ing/
1
Alc
ohol
-Sea
rche
rW
ine-
Sear
cher
Food
Nam
e
Win
e na
me
Win
e na
me
Alc
ohol
Nam
e
Am
eric
an-W
ine-
Sear
cher
Am
eric
an
Win
e N
ame
Cur
renc
y co
nver
ter
Bee
r-Se
arch
er
Bee
r
Nam
e
Pric
e($)
Pric
e($)
Pric
e($)
Pric
e($)
Pric
e/do
llar
Pric
e (y
uan)
12
3
20
Alg
orith
m
W =
(1-λ
) * q
ualit
y ra
te +
(λ) *
sim
ilarit
y va
lue
Qua
lity
rate
can
be
othe
r QoS
mea
sure
men
ts.
Suc
h as
cos
t of t
ime.
21
Alg
orith
m
S1/
1S
2/1
S4/
4S
3/1
S5/
1
S6
D: 1
= (1
+1)
*0.5
D: 1
: D
:2
D: 1
D: 3
.5
vs. D
:3
D: 4
D: 3
.5vs
. D:4
= (2
.+2)
s=3
D:4
.5
λ=0.
5
22
Exa
mpl
e: G
ener
al Ca
ses
λ=
0.3
λ=
0.7
23
Food
-Win
e M
atch
ing
λ=
0.2
λ=
0.8
24
Arc
hite
ctur
e
25
Inte
ract
ive
Com
posit
ion?
Dyn
amic
bin
ding
Effi
cien
t filt
erin
gP
roce
ss M
odifi
ed i
n cu
stom
ized
Pro
cess
Ser
vice
inst
ance
de
term
ined
by
valu
es
prod
uced
at r
untim
e.
26
Hum
an-A
ssist
ed C
ompo
sitio
n
Inte
rface
-mat
chin
g is
not
eno
ugh
for c
ompl
ex
serv
ice
Con
side
r Qua
lity
rate
, cos
t of t
ime,
ge
ogra
phic
regi
on, u
ser p
rofil
e an
d ot
her
attri
bute
s.S
ervi
ce o
utpu
t val
ues
(suc
h as
pric
e of
tick
et)
Tem
plat
e-ba
sed
com
posi
tion
27
Mot
ivat
ing
Exa
mpl
e
Con
side
r a u
ser,
who
is p
lann
ing
a ro
und
trip
to
Lond
on, U
.K.f
rom
Atla
nta,
GA
from
May
1st
to M
ay
15th
.
Initi
ally
, the
sys
tem
dis
play
s a
trave
l pla
nner
for t
he
com
posi
tion
28
Trav
el P
lanne
r
29
Selec
tion
Proc
edur
e
1.S
elec
tion
for s
ervi
ce c
lass
es.
•S
elec
t the
app
ropr
iate
sub
clas
ses
of th
e se
rvic
es
2.S
elec
tion
for s
ervi
ce in
stan
ces.
3.
Sel
ectio
n fo
r nei
ghbo
ring
serv
ices
.
30
Step
I: S
elec
tion
For S
ervi
ce C
lasse
s
Book
ing
Lodg
ing
Tran
spor
tati
on
Flig
ht Tr
ain
Car
Re
ntal
se
rvic
e
Shut
tle
Bus
Taxi
Lim
o
Hot
el
Apa
rtme
nt
31
Step
2: S
elect
ion
for S
ervi
ce In
stan
ces
32
Serv
ice
Nam
e an
d Q
ualit
y Fi
lters
1. S
ervi
ce N
ame
Filte
rke
y-w
ord
sear
chM
ultip
le N
ames
[ME
TEO
R-S
(MW
SD
I)]
2. S
ervi
ce Q
ualit
y R
ate
Filte
rQ
ualit
y ra
te o
ntol
ogy
Sor
ting
algo
rithm
Be
used
indi
vidu
ally
or w
ith o
ther
filte
rs.
33
Geo
grap
hic
Regi
on F
ilter
Wor
ld
Nor
th A
mer
ica
Eur
ope
Asi
a
US
AC
anad
aU
KC
hina
Indi
a
34
IOPE
Sim
ilarit
y Fi
lter (
I)
35
IOPE
Sim
ilarit
y Fi
lter (
II)
36
Pers
onal
Prof
ile F
ilter
The
pers
onal
pro
file
reco
rds
the
hist
ory
of
serv
ice
inst
ance
s us
ed in
volv
ing
usag
e fre
quen
cy b
y th
e us
er.
We
assu
me
that
the
serv
ice
with
hig
hest
us
age
frequ
ency
is m
ost l
ikel
y to
be
sele
cted
in
the
futu
re.
37
Pric
e Fi
lter
38
Step
3:S
elec
tions
for N
eigh
borin
g Se
rvic
es
39
Trip
Com
posit
ion
Serv
ice
Gra
ph
40
Sem
antic
s in
HA
Web
Ser
vice
Ont
olog
y fo
r Web
Ser
vice
cla
ss
sele
ctio
nD
omai
n O
ntol
ogy
for W
eb S
ervi
ces
inst
ance
se
lect
ion,
(qua
lity,
geo
grap
hic
regi
on, I
O
mat
chin
g)W
eb S
ervi
ce N
etw
ork
for N
eigh
borin
g S
ervi
ce S
elec
tions
(fun
ctio
nalit
y S
eman
tics)
41
Cont
ribut
ions
Exp
licit
onto
logi
cal s
ervi
ce d
escr
iptio
ns.
Foun
d op
timal
com
posi
tion
in a
flex
ible
way
(Qos
)D
evel
oped
filte
rs to
hel
p us
ers
to m
ake
bette
r se
rvic
e se
lect
ion
deci
sion
in c
ompo
sitio
n.
Aut
omat
ic C
ompo
sitio
n of
Sem
antic
Web
Ser
vice
s, R
. Zha
ng, B
. A
rpin
ar, a
nd B
. Ale
man
-Mez
a, In
tl. W
eb S
ervi
ces
Con
fere
nce,
Las
V
egas
NV
, 200
3.
Ont
olog
y-D
riven
Web
Ser
vice
s C
ompo
sitio
n, B
. Arp
inar
, R. Z
hang
, B.
Ale
man
-Mez
a, a
nd A
. Mad
uko,
IEE
E C
onfe
renc
e on
E-C
omm
erce
Te
chno
logy
(CE
C 2
004)
, San
Die
go, C
alifo
rnia
, Jul
y 6-
9, 2
004
(acc
epte
d).
42
Futu
re W
ork:
Fun
ctio
nalit
y-ba
sed
Com
posit
ion
Com
posi
tion
base
d on
thei
r int
erna
l com
puta
tions
w
hen
thei
r pro
files
may
not
con
vey
adeq
uate
se
man
tics
to d
iffer
entia
te th
em.
Som
e th
ough
ts:
Bla
ck-b
ox a
ppro
ach:
Exp
loit
pre-
and
post
-con
ditio
ns in
com
posi
tion
Whi
te-b
ox a
ppro
ach:
P
roce
ss o
ntol
ogy
Sta
te tr
ansf
orm
atio
ns (e
.g.,
Pet
ri ne
ts)
Pro
cess
Que
ry L
angu
age
Kle
in
43
Refe
renc
e
[Sriv
asta
va03
] B. S
rivas
tava
, J.K
oehl
er. W
eb S
ervi
ce C
ompo
sitio
n –c
urre
nt
solu
tions
and
ope
n pr
oble
m. I
caps
2003
Wor
ksho
p on
Pla
nnin
g fo
r Web
S
ervi
ces
Ada
ptin
g G
olog
for P
rogr
amm
ing
the
Sem
antic
Web
.S. M
cIlra
ith, T
.C. S
on.
[Kle
in01
]M. K
lein
, and
A. B
erns
tein
. Sea
rchi
ng fo
r Ser
vice
s on
the
Sem
antic
W
eb U
sing
Pro
cess
Ont
olog
ies,
Inte
rnat
iona
l Sem
antic
Web
Wor
king
S
ympo
sium
, Aug
ust 2
001.
44
Que
stio
ns?
45
Than
ks
