Through

Different

Eyes

Assessing

Multiple

Conceptual

Views

for

Querying

Web

Services

We

present

enhancements

for

UDDI

DAML-S

registries

allowing

cooperative

discovery

and

selection

of

Web

services

with

a

focus

on

personalization

To

find

the

most

useful

service

in

each

instance

of

a

request

not

only

explicit

parameters

of

the

request

have

to

be

matched

against

the

service

offers

Also

user

preferences

or

implicit

assumptions

of

a

user

with

respect

to

common

knowledge

in

a

certain

domain

have

to

be

considered

to

improve

the

quality

of

service

provisioning

In

the

area

of

Web

services

the

notion

of

service

ontologies

together

with

cooperative

answering

techniques

can

take

a

lot

of

this

responsibility

However

without

quality

assessments

for

the

relaxation

of

service

requests

and

queries

a

personalized

service

discovery

and

selection

is

virtually

impossible

This

paper

focuses

on

assessing

the

semantic

meaning

of

query

relaxation

plans

over

multiple

conceptual

views

of

the

service

ontology

each

one

representing

a

soft

query

constraint

of

the

user

request

Our

focus

is

on

the

question

what

constitutes

a

minimum

amount

of

necessary

relaxation

to

answer

each

individual

request

in

a

cooperative

manner

Incorporating

such

assessments

as

early

as

possible

we

propose

to

integrate

ontology-based

discovery

directly

into

UDDI

directories

or

query

facilities

in

service

provisioning

portals

Using

the

quality

assessments

presented

here

this

integration

promises

to

propel

today

's

Web

services

towards

an

intuitive

user-centered

service

provisioning

H.

-LSB-

Information

Storage

and

Retrieval

-RSB-

Online

Information

Services

Web-based

services

Algorithms

Human

Factors

Design

Web

services

are

expected

to

provide

an

open

platform

not

only

for

electronic

interaction

but

also

for

the

provisioning

of

so-called

user-centered

services

i.e.

services

that

can

provide

useful

information

and

a

variety

of

service

offers

to

support

users

in

a

modern

mobile

lifestyle

Though

the

capabilities

of

such

services

are

still

relatively

simple

their

sophistication

will

grow

with

the

improvement

of

-LRB-

wireless

-RRB-

networks

bandwidths

and

client

device

capabilities

However

finding

the

adequate

service

for

subsequent

use

of

each

individual

user

becomes

a

more

and

more

demanding

problem

Given

the

convergence

of

networks

in

forthcoming

-LRB-

mobile

-RRB-

environments

and

the

evolving

innovative

business

models

for

third

party

service

deployment

-LRB-

e.g.

NTT

DoCoMo

's

i-mode

service

certification/licensing

model

for

mobile

service

portals

-LSB-

-RSB-

-RRB-

the

variety

of

services

is

even

expected

to

grow

Making

an

informed

choice

of

the

right

service

will

therefore

include

matching

individual

users

preferences

or

dislikes

against

the

concepts

and

capabilities

of

the

services

offered

Usually

the

interaction

process

for

Web

services

consists

of

three

distinct

phases

a

discovery

of

possible

services

the

selection

of

the

most

useful

and

the

subsequent

execution

In

understanding

what

a

service

actually

offers

the

first

two

phases

are

crucial

and

the

general

acceptance

of

user-centered

services

will

depend

on

the

solutions

of

still

demanding

problems

in

interaction

like

cooperative

querying

As

sh

own

in

-LSB-

-RSB-

and

-LSB-

-RSB-

the

discovery

and

selection

processes

of

user-centered

Web

services

involves

a

high

degree

of

respect

for

user

preferences

to

be

flexible

enough

for

real

world

use

In

that

respect

providing

user-centered

services

strongly

differs

from

the

well-defined

capabilities

of

traditional

services

As

a

running

example

of

a

typical

user-centered

service

we

will

use

an

extension

of

the

cooperative

restaurant

booking

Web

service

presented

in

-LSB-

-RSB-

restaurant

booking

services

subscribe

to

the

least

general

applicable

node

along

a

complex

service

ontology

for

a

number

of

characteristics

A

service

request

can

then

be

performed

including

a

choice

of

various

individual

categories

However

the

individual

services

offered

will

usually

only

more

or

less

match

all

the

user

's

expectations

Ranking

services

with

respect

to

requests

is

thus

an

ongoing

challenge

as

is

also

evident

from

the

research

areas

of

IR

or

Web

search

engines

for

information

provisioning

Service

providers

almost

always

can

anticipate

some

typical

interactions

with

their

services

For

our

example

typical

tasks

are

for

instance

booking

a

certain

restaurant

for

a

specific

evening

finding

a

suitable

restaurant

in

the

vicinity

for

lunch

etc.

The

characteristics

and

input

parameters

for

Web

services

for

restaurant

booking

thus

usually

contain

a

number

of

general

input

values

that

can

be

specified

in

a

service

request/query

the

name

of

the

restaurant

its

location

its

specific

address

the

type

of

cuisine

the

date

and

time

for

a

booking

its

price

range

or

even

third

party

content

like

recommendations

-LRB-

e.g.

the

Zagat

reviews

-RRB-

However

from

a

service

provisioning

point

of

view

the

nature

of

these

parameters

strongly

differs

A

user

expecting

to

book

a

certain

restaurant

on

a

specific

evening

will

expect

that

the

request

may

be

Copyright

is

held

by

the

author/owner

-LRB-

s

-RRB-

WWW

May

New

York

NY

USA

ACM

granted

or

may

fail

depending

on

current

reservations

of

that

restaurant

for

the

given

date

but

relaxing

the

constraints

of

the

date

given

or

booking

a

different

restaurant

for

the

evening

might

simply

not

do

In

contrast

a

user

simply

wishing

for

a

close-by

restaurant

to

have

lunch

will

rarely

provide

such

fixed

terms

as

a

restaurants

name

but

rather

use

descriptive

terms

like

a

preferred

cuisine

and

an

approximate

location

service

request

service

providers

service

n

implementation

service

implementation

service

providers

service

n

implementation

service

implementation

e

x

ecutio

n

UDDI

registry

service

interface

service

n

interface

UDDI

registry

service

interface

service

n

interface

quality

assessment

service

ontologies

choice

service

descriptions

user

profile

matchmaking

Figure

Concept

of

enhanced

UDDI

service

registries

Distinguishing

such

query

stereotypes

like

book

a

table

at

the

Chez

Panisse

for

the

pm

and

give

me

the

name

and

address

of

a

Chinese

restaurant

in

the

commercial

district

of

San

Francisco

with

medium

price

range

and

the

subsequent

personalization

of

service

provisioning

also

needs

different

types

of

input

parameters

Whereas

simple

variables

like

the

restaurant

's

name

or

a

certain

category

in

a

clear

request

can

be

handled

in

an

exact

match

fashion

more

fuzzy

attributes

in

a

somewhat

tentative

request

like

an

approximate

location

or

the

choice

of

cuisine

have

to

be

understood

as

a

user

s

preferences

with

respect

to

certain

concepts

-LRB-

soft

constraints

-RRB-

In

the

area

of

the

Semantic

Web

the

management

of

such

concepts

is

usually

done

by

the

very

powerful

tool

of

ontologies

that

describe

a

generalization

hierarchy

of

such

concepts

In

the

course

of

this

paper

we

will

show

how

to

open

up

service

provisioning

to

the

better

understanding

adequate

handling

and

quality

assessment

of

each

individual

user

's

intentions

and

preferences

The

contribution

of

the

paper

thus

is

twofold

On

one

hand

we

relate

the

use

of

ontologies

and

the

handling

of

conceptual

views

like

given

by

the

Semantic

Web

to

co-operatively

evaluating

preferences

for

each

specific

user

On

the

other

hand

we

show

how

to

effectively

deal

with

the

problem

of

relaxing

multiple

conceptual

views

for

more

complex

queries

and

give

quality

measures

to

assess

the

most

useful

results

for

each

specific

user

Both

contributions

can

be

expected

to

improve

the

service

provisioning

of

user-centered

Web

services

and

help

to

boost

their

usability

and

thus

subsequently

their

acceptance

Today

Web

services

are

usually

provided

via

an

Internet

wide

network

of

services

registries

given

by

the

Universal

Description

Discovery

and

Integration

-LRB-

UDDI

-RRB-

-LSB-

-RSB-

UDDI

builds

on

the

Web

Service

Definition

Language

WSDL

-LSB-

-RSB-

which

features

basic

information

about

providers

of

a

service

and

technical

service

invocation

details

Even

though

UDDI

has

become

the

de

facto

standard

in

the

field

it

suffers

from

a

major

shortcoming

the

information

offered

on

individual

services

is

rather

limited

A

yellow

page-style

lookup

mechanism

provides

the

service

interface

together

with

a

short

verbal

description

of

what

task

the

service

performs

Mainly

targeted

a

human

Web

service

experts

and

developers

advanced

query

capabilities

and

cooperative

matchmaking

however

are

still

lacking

Research

in

the

area

of

the

Semantic

Web

seeks

a

solution

to

this

unsatisfy

ing

situation

e.g.

-LSB-

-RSB-

-LSB-

-RSB-

Generally

speaking

the

Semantic

Web

fosters

a

population

of

the

Web

with

content

and

services

having

formal

semantics

and

rich

service

descriptions

Several

semantic

frameworks

for

Web

services

are

currently

emerging

with

DAML-S

-LSB-

-RSB-

and

's

recently

established

OWL-S

-LSB-

-RSB-

-LSB-

-RSB-

initiative

as

the

most

prominent

approaches

We

have

built

our

previous

work

on

DAML-S

as

a

relatively

mature

ontology-based

approach

to

the

description

of

Web

services

that

tries

to

provide

a

common

ontology

of

services

for

the

Semantic

Web

Building

on

top

of

DAML+OIL

-LSB-

-RSB-

the

Web

service

representations

in

DAML-S

consist

of

a

service

profile

for

advertising

and

discovering

services

a

process

model

giving

a

detailed

description

of

a

service

's

operation

and

a

service

grounding

providing

details

on

how

to

interoperate

with

services

via

message

exchange

Figure

shows

a

schematic

view

of

our

semantically

enriched

Web

service

provisioning

concept

A

user

states

personal

needs

and

preferences

in

an

enhanced

service

request

The

enhanced

UDDI

registry

matches

this

request

against

the

descriptions

of

all

registered

services

The

actual

matching

can

be

carried

out

using

cooperative

database

technology

like

sh

own

in

-LSB-

-RSB-

The

query

is

split

in

hard

and

soft

constraints

where

the

hard

constraints

are

processed

as

filter

conditions

whereas

the

soft

conditions

can

be

relaxed

if

necessary

If

no

user-specific

preferences

are

given

with

the

service

request

the

relaxation

follows

the

domain-specific

conceptual

views

of

the

service

ontology

given

by

the

service

providers

or

portal

operators

To

distinguish

between

several

possible

relaxations

the

quality

assessment

which

is

the

main

aspect

of

this

paper

will

evaluate

the

degree

of

match

for

each

service

with

respect

to

the

original

user

query

and

offer

all

best

matches

After

a

certain

implementation

has

been

chosen

the

service

provider

will

execute

the

service

and

deliver

the

result

From

a

service

provisioning

viewpoint

centralized

and

publicly

available

service

ontologies

can

be

understood

as

a

default

service

conceptualization

or

the

most

common

service

concept

hierarchy

i.e.

encoding

common

and

widely

accepted

knowledge

or

world/domain

knowledge

Due

to

the

hierarchical

nature

of

ontologies

a

user

asking

for

a

specific

service

will

also

be

served

with

any

more

specific

service

concept

subsumed

by

his

request

On

the

other

hand

in

the

case

where

a

best

match

to

his

initial

request

is

not

available

he/she

might

also

be

satisfied

with

more

general

services

from

a

super-class

of

the

requested

one

This

is

determined

by

a

relaxation

step

in

the

service

ontology

i.e.

a

generalization

of

concepts

along

the

lines

of

the

ontology

In

this

section

we

provide

a

brief

overview

of

the

use

of

ontologies

for

relaxation

of

soft

query

constraints

and

show

how

common

knowledge

serves

as

a

default

for

cooperative

retrieval

American

California

Seafood

Shopping

Commercial

Cajun

Texmex

Organic

Fusion

Center

Suburb

City

Cultural

Location

Thing

Restaurant

Akasaka

Chez

Panisse

Avocado

Garden

Cesar

The

Walrus

Se

rvi

c

e

s

S

e

rvi

c

e

O

n

t

o

lo

gy

Cuisine

Figure

Service

Ontology

for

restaurant

booking

Service

Ontologies

User

Preferences

and

Usage

Patterns

The

purpose

of

a

service

ontology

is

to

describe

the

kinds

of

entities

available

in

a

service

repository

and

how

they

are

related

To

this

end

service

ontologies

may

include

descriptions

of

service

classes

properties

and

their

instances

-LRB-

the

actual

services

that

are

eventually

selected

for

execution

-RRB-

A

basic

service

ontology

is

depicted

in

Figure

Here

restaurant

booking

services

are

classified

according

to

their

cuisine

and

their

location

in

a

city

For

instance

the

restaurant

Chez

Panisse

serves

Organic

food

with

Organic

being

a

specialization

of

the

Californian

cuisine

-LRB-

as

well

as

of

American

-RRB-

Furthermore

the

restaurant

Chez

Panisse

is

located

in

the

Shopping

district

which

itself

is

part

of

the

city

Center

We

have

used

's

Web

Ontology

Language

-LRB-

OWL

-RRB-

and

its

predecessor

DAML+OIL

to

enrich

DAML-S

service

pro-

A

href

html

files

in

Web

service

repositories

-LSB-

-RSB-

-LSB-

-RSB-

Modeled

in

OWL

the

most

general

Restaurant

and

Location

concepts

are

anchored

in

owl

Thing

the

most

common

concept

of

any

ontology

A

restaurant

booking

service

using

the

service

ontology

from

Figure

might

on

one

hand

assume

that

a

user

asking

for

a

restaurant

featuring

American

cuisine

will

be

well

served

by

all

restaurants

with

e.g.

Cajun

Californian

or

Texmex

cuisine

since

they

are

all

instantiations

of

American

cuisines

On

the

other

hand

if

a

user

asks

for

a

Californian

fusion

cuisine

restaurant

and

no

such

service

should

be

registered

implicitly

relaxing

the

query

to

all

Californian

restaurants

and

offering

restaurants

with

organic

cuisine

or

Californian

seafood

to

our

user

will

be

more

helpful

than

just

stating

the

empty

result

And

even

if

a

user

has

different

conceptions

-LRB-

e.g.

cuisines

being

related

based

on

their

flavors

-RRB-

or

explicit

preferences

-LRB-

a

Chinese

restaurant

rather

than

an

Italian

one

-RRB-

an

ontology-based

discovery/selection

model

is

still

useful

-LSB-

-RSB-

shows

in

detail

how

to

deal

with

these

cases

by

overwriting

the

default

ontology

with

an

explicitly

provided

-LRB-

or

implicitily

derived

-RRB-

generalization

hierarchy

of

a

user

somewhat

similar

to

the

view

definitions

proposed

by

-LSB-

-RSB-

Since

for

our

assessment

framework

here

the

exact

kind

and

classes/values

of

an

ontology

do

matter

less

than

its

actual

structure

in

each

instance

such

overwritings

by

user

specified

conceptions

are

always

possible

to

facilitate

We

advocate

the

use

of

service

ontologies

together

with

a

proprietary

notion

of

basic

user

preferences

and

typical

service

usage

patterns

for

the

stepwise

refinement

of

service

requests

in

a

cooperative

service

provisioning

environment

While

the

basic

approach

and

combination

of

ontologies

preferences

and

patterns

is

published

elsewhere

-LSB-

-RSB-

we

will

now

concentrate

on

enhancements

to

the

relaxation

along

the

lines

of

ontologies

alone

unlike

the

specialization

of

a

request

a

generalization

can

result

in

severe

changes

of

the

initial

query

semantics

This

is

especially

true

if

several

relaxation

steps

have

to

be

performed

until

a

match

can

be

found

At

the

point

of

relaxing

a

constraint

to

the

root

of

an

ontology

the

respective

constraint

can

even

be

considered

as

entirely

dropped

But

nevertheless

since

an

ontology

resembles

common

knowledge

-LRB-

and

thus

implicit

preferences

-RRB-

for

high

quality

service

provisioning

offering

somewhat

related

features

is

usually

still

a

better

default

than

just

returning

an

empty

result

set

Multiple

Conceptual

Views

Individual

users

might

have

quite

specific

ideas

about

differing

domain

concepts

-LRB-

conceptions

-RRB-

or

very

clear

expectations

how

to

be

served

differing

from

the

usual

domain

assumptions

-LRB-

explicit

preferences

-RRB-

but

also

implicit

preferences

play

an

important

part

Consider

for

instance

location-based

services

e.g.

for

restaurant

booking

If

a

user

asks

to

book

a

Chinese

restaurant

for

dinner

the

common

domain

knowledge

tells

us

that

this

restaurant

should

be

in

the

vicinity

-LRB-

e.g.

a

miles

area

-RRB-

of

his

current

or

usual

whereabouts

and

we

can

add

this

information

as

an

implicit

constraint

for

better

provisioning

quality

A

user

in

San

Francisco

would

usually

be

annoyed

by

offers

of

Chinese

restaurants

in

Hong

Kong

no

matter

how

good

their

actual

quality

or

rating

is

If

this

general

assumption

would

not

hold

however

-LRB-

e.g.

if

a

user

wants

to

fly

to

Hong

Kong

in

the

morning

and

then

have

dinner

there

-RRB-

he

or

she

would

have

stated

this

unusual

detail

already

within

the

query

and

had

asked

for

a

Chinese

restaurant

in

Hong

Kong

for

dinner

Such

explicit

information

within

a

service

request

is

provided

due

to

the

psychological

notion

that

though

users

want

a

service

to

know

what

is

sensible

-LRB-

like

they

expect

to

be

served

in

human-human

interaction

-RRB-

no

user

expects

a

service

to

be

clairvoyant

Thus

not

only

having

further

-LRB-

explicit

-RRB-

knowledge

of

a

user

but

also

assuming

typical

behavior

concept

archies

given

by

ontologies

can

be

used

as

good

default

relaxation

hierarchies

for

user

preferences

Should

however

some

preferences

or

a

specific

conception

be

given

the

underlying

ontology

has

to

be

exchanged

against

the

user-provided

terms

or

concepts

We

introduce

the

notion

of

conceptual

views

on

a

service

ontology

to

account

for

all

the

different

interests

a

user

wants

to

express

in

a

service

request

Such

a

conceptual

view

is

modeled

as

a

clipping

from

the

full

service

ontology

that

starts

with

the

most

general

concept

associable

with

a

specific

user

interest

For

this

paper

we

will

for

the

ease

of

understanding

assume

conceptual

views

to

be

non-overlapping

tree-shaped

clippings

from

the

full

service

ontology

where

each

service

is

registered

with

the

node

that

describes

its

value

with

respect

to

the

most

specific

characteristics

An

example

conceptual

view

is

indicated

as

a

grey

shaping

in

figure

the

view

named

Cuisine

is

basically

a

sub-ontology

only

concerned

with

the

classification

of

restaurants

according

to

the

offered

type

of

food

Whereas

the

restaurants

The

Walrus

Chez

Panisse

Avocado

Garden

and

Cesar

are

classified

as

being

Fusion

or

Organic

places

the

restaurant

Akasaka

is

not

reachable

in

this

view

as

it

is

only

classified

as

located

in

the

cultural

district

As

we

will

discuss

in

the

remainder

of

this

paper

multiple

conceptual

views

can

be

used

to

account

for

different

interests

a

user

wants

to

express

in

a

service

request

and

the

relative

importance

between

them

In

the

case

of

the

restaurant

booking

example

it

is

conceivable

that

a

user

values

the

fulfillment

of

location

constraints

over

cuisine

constraints

if

he/she

is

only

up

for

a

quick

work

lunch

However

for

the

ambitious

hobby

gour-met

this

might

be

just

the

other

way

round

on

the

weekend

Thus

we

will

need

ways

to

assess

the

respective

quality

of

different

relaxation

schemes

to

allow

users

to

make

an

informed

choice

Let

us

now

focus

on

problems

that

arise

when

multiple

soft

constraints

have

to

be

relaxed

over

the

service

ontology

We

will

first

look

at

our

sample

scenario

investigate

the

relaxation

of

conceptual

views

and

then

discuss

some

quality

considerations

Relaxation

Plans

for

Multiple

Selection

Predicates

Let

us

consider

our

restaurant

booking

service

from

above

A

typical

query

would

be

Find

a

Californian

fusion

cuisine

restaurant

in

the

commercial

district

of

Berkeley

Here

we

will

have

to

deal

with

two

soft

constraints

the

type

of

cuisine

and

the

location

Assuming

that

we

do

not

have

more

specific

information

about

the

user

's

preferences

both

constraints

could

be

relaxed

along

the

two

default

conceptual

views

of

the

service

ontology

of

figure

The

Walrus

fusion

Cesar

Chez

Panisse

californian

organic

Avocado

Garden

Cucina

Calabrese

seafood

The

Mediterraneum

Figure

The

cuisine

ontology

with

respective

instances

Figures

and

show

the

full

first

two

concept

levels

of

the

respective

conceptual

views

with

some

instances

So

in

figure

we

can

for

instance

see

that

there

is

a

service

for

a

restaurant

called

The

Walrus

which

is

classified

as

offering

fusion

cuisine

and

as

such

also

offering

Californian

cuisine

The

relaxation

of

query

predicates

over

such

a

view

is

straightforward

If

the

query

predicate

specifies

fusion

cuisine

restaurants

we

would

have

the

choice

between

the

respective

instances

here

The

Walrus

and

Cesar

If

for

some

reason

there

would

be

no

fusion

cuisine

restaurants

registered

or

the

instances

can

not

satisfy

some

other

constraints

-LRB-

like

booking

for

a

certain

date

-RRB-

we

will

relax

along

the

ontology

to

the

more

general

concept

of

Californian

cuisine

and

can

also

consider

the

restaurants

that

are

registered

under

the

organic

and

seafood

characterizations

of

California

cuisine

The

technical

problem

of

how

to

relax

single

conceptual

views

with

adequate

query

languages

over

cooperative

database

systems

is

in

detail

addressed

e.g.

in

-LSB-

-RSB-

and

-LSB-

-RSB-

The

beauty

of

the

design

is

that

all

details

of

a

UDDI

or

DAML-S

style

description

for

each

service

together

with

some

more

characteristics

-LRB-

e.g.

taken

from

RDF

statements

of

a

restaurants

homepage

-RRB-

can

be

stored

in

a

classic

relational

database

by

the

service

provider

and

can

be

searched

using

a

declarative

query

language

extended

by

preference

constructors

like

sh

own

in

e.g.

-LSB-

-RSB-

Thus

an

added-value

service

using

semantically

meaningful

content

can

be

provided

quite

easily

Tom

's

Grill

commercial

Sushi

and

Soul

Avocado

Garden

city

center

cultural

The

Mediterraneum

Akasaka

Chez

Panisse

shopping

Cesar

Pizza

My

Heart

Figure

The

location

ontology

with

respective

instances

A

more

serious

problem

arises

when

several

soft

query

constraints

over

various

conceptual

views

have

to

be

evaluated

Consider

for

instance

the

query

on

a

fusion

cuisine

restaurant

in

the

commercial

district

We

can

easily

verify

that

in

our

example

even

though

The

Walrus

and

Cesar

are

fusion

cuisine

they

are

not

registered

in

the

commercial

district

Likewise

Tom

's

Grill

Sushi

and

Soul

and

Avocado

Garden

are

in

the

commercial

district

but

they

do

not

offer

fusion

cuisine

Aiming

at

a

cooperative

retrieval

behavior

we

are

left

with

three

choices

relaxing

the

constraint

on

the

cuisine

relaxing

the

constraint

on

the

location

or

relaxing

both

constraints

When

relaxing

the

cuisine

to

califor-nian

we

would

retrieve

the

service

of

Avocado

Garden

the

only

Californian

cuisine

restaurant

within

the

commercial

district

Relaxing

the

location-based

ontology

would

result

only

in

the

service

of

Cesar

the

only

fusion

cuisine

restaurant

in

the

city

center

Finally

relaxing

both

constraints

would

result

in

all

Californian

cuisine

restaurant

within

the

city

center

i.e.

Avocado

Garden

The

Mediterraneum

Chez

Panisse

and

Cesar

Thus

different

kinds

of

relaxation

will

usually

result

in

essentially

differing

answer

sets

This

problem

will

remain

if

more

relaxation

steps

have

to

be

taken

If

we

have

relaxed

only

one

constraint

we

could

e.g.

decide

to

relax

this

property

even

further

or

relax

another

property

of

our

service

during

the

next

step

Let

us

first

define

the

task

of

finding

the

best

service

under

relaxation

The

Problem

of

Best-Matching

Service

Provisioning

Given

various

characteristics

that

describe

all

services

in

a

service

ontology

a

concept

hierarchy

-LRB-

conceptual

view

-RRB-

for

each

characteristic

and

a

user

request

stating

a

number

of

hard

and

soft

constraints

the

best-matching

service

is

given

by

all

services

that

fulfill

all

the

hard

constraints

fulfill

all

soft

constraints

with

a

minimum

amount

of

relaxation

of

characteristics

with

respect

to

a

suitable

quality

measure

So

given

that

each

service

is

registered

in

one

concept

node

for

each

characteristic

given

by

the

respective

conceptual

view

and

all

services

not

fulfilling

the

hard

constraints

have

been

filtered

the

problem

of

selection

over

multiple

constraints

comes

down

to

deciding

what

is

a

minimum

amount

of

relaxation

Obviously

the

basic

task

is

finding

a

service

that

is

registered

with

all

concepts

-LRB-

or

any

of

their

respective

sub-concepts

-RRB-

as

specified

by

the

query

a

perfect

match

But

if

there

is

no

such

service

registered

the

decision

what

soft

constraints

to

relax

and

how

far

they

are

relaxed

is

paramount

for

the

quality

of

provisioning

Basic

Service

Quality

Considerations

Since

the

decision

about

the

relaxation

scheme

is

important

for

the

output

some

way

of

considering

which

scheme

to

follow

is

needed

Usually

ontologies

are

of

a

qualitative

nature

A

superclass

subclass

taxonomy

is

established

but

there

is

no

knowledge

of

the

degree

or

the

relative

distance

between

different

concepts

However

such

knowledge

could

crucially

change

the

utility

of

certain

relaxation

plans

Relaxing

more

refined

ontologies

or

views

will

generally

hurt

the

user

preferences

less

than

relaxing

already

coarse

views

or

ontologies

The

less

general

the

concept

the

more

refined

are

the

sets

of

objects

that

will

be

offered

to

the

user

and

flooding

the

user

with

too

much

too

general

content

is

avoided

Let

us

first

take

a

closer

look

at

merely

qualitative

views

on

ontologies

of

comparable

granularity

etc.

-LRB-

i.e.

relaxing

one

constraint

is

introducing

the

same

amount

of

generalization

as

relaxing

any

other

-RRB-

and

then

investigate

ways

to

deal

with

quantitative

measures

in

the

following

section

For

scenarios

of

merely

qualitative

preferences

and

their

relaxation

for

the

restricted

class

of

ceteris

paribus

preferences

-LSB-

-RSB-

proposes

a

scheme

of

ordering

different

objects

in

the

result

set

according

to

the

count

of

necessary

relaxation

steps

from

the

top

or

the

bottom

of

the

hierarchy

or

simply

the

relative

distance

to

all

violated

query

constraints

Since

we

assumed

symmetrical

views

on

ontologies

also

in

our

relaxation

problem

a

similar

concept

will

help

us

to

understand

what

should

be

relaxed

preferably

and

what

this

means

for

the

objects

in

the

result

set

Let

us

first

show

the

approach

of

simply

counting

the

relaxation

steps

from

the

violated

constraints

We

will

label

the

services

we

found

in

each

step

by

the

number

of

necessary

steps

to

find

them

But

first

we

need

to

define

the

necessary

concept

of

relaxation

paths

Given

tree-shaped

conceptual

views

of

a

service

ontology

that

arranges

concepts

or

values

with

respect

to

certain

service

characteristics

using

a

generalization

semantics

a

relaxation

path

is

a

path

along

the

edges

of

each

conceptual

view

that

leads

from

a

base

concept

to

the

respective

root

of

the

view

Usually

this

base

concept

is

specified

in

a

service

request

or

user

query

and

relaxing

along

the

relaxation

path

leads

to

an

increasing

generalization

of

this

concept

Assuming

that

all

services

have

been

assigned

to

the

node

of

their

first

appearance

along

the

relaxation

path

-LRB-

i.e.

they

are

registered

to

any

of

the

respective

node

's

sub-trees

of

concepts

but

not

to

an

earlier

node

of

the

relaxation

path

-RRB-

we

get

a

chain

of

concepts

with

all

services

registered

under

the

aspect

of

least

necessary

level

of

generalization

An

example

for

a

relaxation

path

can

be

easily

derived

from

figures

and

If

a

user

is

primarily

interested

in

the

commercial

district

the

appropriate

nodes

of

the

relaxation

path

would

be

commercial

district

city

center

city

and

location

The

services

registered

in

the

nodes

are

e.g.

Tom

's

Grill

Sushi

and

Soul

and

Avocado

Garden

for

commercial

district

The

node

city

center

would

also

contain

all

services

registered

to

its

sub-concepts

-LRB-

i.e.

The

Mediterraneum

Akasaka

Chez

Panisse

Cesar

and

Pizza

My

Heart

-RRB-

and

so

on

Figure

shows

the

first

two

steps

of

respective

relaxation

paths

for

both

conceptual

views

in

figures

and

focusing

only

on

the

services

registered

in

both

views

As

we

pointed

out

we

will

always

assume

tree-shape

views

for

the

course

of

this

paper

Please

note

that

all

the

concepts

easily

can

be

transferred

to

the

case

where

sub-concepts

can

have

multiple

parent

nodes

In

this

case

the

node

along

the

relaxation

path

would

consist

of

the

intersection

of

the

different

parent

concepts

or

the

intersection

of

their

registered

services

respectively

This

generalization

has

already

been

successfully

employed

in

a

similar

fashion

for

mapping

queries

between

differing

ontologies

by

-LSB-

-RSB-

Distances

then

can

simply

be

measured

by

the

minimum

distance

if

multiple

paths

for

relaxation

should

be

available

Let

us

now

see

how

relaxation

can

be

done

using

an

unlabeled

relaxation

graph

-LRB-

see

figures

and

-RRB-

If

we

begin

by

either

relaxing

the

cuisine

or

the

location

constraint

of

our

query

we

would

have

to

assign

a

quality

value

of

to

both

the

Avocado

Garden

and

Cesar

Thus

in

terms

of

quality

they

are

incomparable

which

closely

resembles

our

missing

knowledge

of

what

kind

of

relaxation

the

individual

user

would

prefer

If

there

should

be

more

ways

to

relax

constraints

to

encounter

a

service

we

will

always

count

the

minimum

number

of

relaxation

steps

necessary

Relaxing

both

constraints

again

leads

to

quality

values

of

for

Avocado

Garden

and

Cesar

and

a

value

of

for

The

Mediterraneum

and

Chez

Panisse

because

they

have

only

been

seen

by

relaxing

both

constraints

and

thus

are

probably

less

desirable

for

the

user

However

relaxing

the

cuisine

ontology

two

steps

-LRB-

i.e.

to

all

American

cuisines

-RRB-

and

sticking

to

the

commercial

center

constraint

might

result

in

Tom

's

Grill

turning

up

also

with

a

value

of

This

is

because

in

the

ve

model

the

semantic

difference

between

deep

relaxation

and

broad

relaxation

is

considered

the

same

To

be

sensitive

to

the

differing

implications

of

broad

and

deep

relaxation

with

respect

to

generality

we

will

use

our

concept

of

relaxation

paths

and

show

how

to

use

labels

along

this

path

to

get

to

a

more

sophisticated

relaxation

paradigm

fusion

californian

american

city

center

commercial

city

fusion

californian

american

city

center

commercial

city

Avocado

Garden

Tom

s

Grill

Cesar

Cesar

Avocado

Garden

Tom

s

Grill

Chez

Panisse

Mediterraneum

Chez

Panisse

Mediterraneum

Figure

Relaxation

paths

and

assigned

services

Usually

the

generalization

throughout

ontologies

will

become

quickly

rather

unspecific

with

decreasing

distance

to

the

root

Hence

a

broad

relaxation

strategy

-LRB-

breadth

first

relaxation

-RRB-

often

is

preferred

to

deep

relaxation

steps

Summing

up

the

distances

like

before

but

weighing

each

relaxation

step

with

the

relative

distance

to

the

original

query

term

can

implement

this

Consider

our

query

for

fusion

cuisine

restaurants

in

the

commercial

district

So

for

example

the

Avocado

Garden

and

Cesar

need

each

only

one

relaxation

step

with

a

distance

of

one

to

the

original

constraint

-LRB-

cf.

labels

in

figure

-RRB-

so

their

quality

value

is

Chez

Panisse

and

The

Mediterraneum

both

need

two

relaxation

steps

with

a

distance

of

each

resulting

in

a

value

of

In

contrast

Tom

's

Grill

also

needs

only

two

relaxation

steps

but

whereas

the

first

step

has

a

distance

of

the

second

step

already

shows

a

distance

of

Thus

the

final

value

for

Tom

's

Grill

is

-LRB-

i.e.

-RRB-

and

we

can

now

effectively

distinguish

between

deep

and

broad

relaxation

Depending

on

the

nature

and

granularities

of

the

ontology

or

views

we

can

of

course

also

use

higher

weightings

for

the

deep

relaxations

for

instance

-LRB-

-RRB-

So

the

first

step

will

be

weighted

by

the

second

deep

step

by

the

third

deep

step

by

and

so

on

Since

the

broad

relaxation

steps

are

still

simply

added

up

this

will

punish

deep

relaxation

and

avoid

too

broad

generalizations

of

constraints

If

we

always

want

to

punish

deep

steps

symmetrically

until

all

constraints

in

turn

are

relaxed

at

least

to

the

same

level

a

factor

of

-LRB-

-RRB-

-LRB-

-RRB-

will

be

adequate

as

shown

in

the

following

lemma

Lemma

Weightings

to

Foster

Broad

Ontology

Relaxation

Given

n

soft

query

constraints

with

their

respective

relaxation

hierarchies

To

always

prefer

a

broad

relaxation

scheme

label

each

object

by

summing

up

the

numbers

of

edges

relaxed

to

find

this

object

in

each

hierarchy

and

weigh

every

edge

by

n

-LRB-

d

-RRB-

using

the

number

of

soft

constraints

n

and

the

relative

distance

d

to

the

original

query

constraint

Proof

Since

within

each

depth

all

weightings

are

the

same

it

is

obvious

that

within

a

certain

depth

of

the

hierarchy

any

object

seen

with

less

relaxation

steps

has

a

smaller

label

than

an

object

that

needs

more

steps

Thus

if

we

e.g.

have

to

relax

two

constraints

within

a

level

this

object

will

always

be

labeled

with

a

higher

weight

than

an

object

that

needed

only

one

relaxation

independently

of

which

constraints

have

been

relaxed

We

still

have

to

show

that

if

we

do

a

step

with

a

deeper

distance

an

object

O

encountered

there

always

gets

a

higher

label

than

any

object

P

encountered

in

all

hierarchies

only

with

relaxations

up

to

a

lower

distance

We

will

do

that

by

showing

that

the

minimum

label

for

object

O

is

higher

than

the

maximum

label

for

object

P.

Let

us

assume

that

in

order

to

encounter

object

O

we

have

to

relax

at

least

one

constraint

to

a

distance

of

k

The

minimum

label

for

O

thus

is

given

by

relaxing

only

a

single

constraint

to

distance

k

and

not

having

to

relax

any

other

constraint

Hence

object

O

's

label

is

given

by

-LRB-

n

n

n

-LRB-

-RRB-

n

-LRB-

-RRB-

-RRB-

The

maximum

possible

label

for

object

P

on

the

other

hand

is

given

by

having

to

relax

every

of

the

n

constraints

-LRB-

-RRB-

times

i.e.

the

maximum

distance

smaller

than

k.

Thus

the

maximum

label

for

P

is

n

-LRB-

n

n

n

-LRB-

-RRB-

-RRB-

-LRB-

n

n

-LRB-

-RRB-

n

-LRB-

-RRB-

-RRB-

and

thus

P

's

label

is

even

relaxing

all

constraints

to

a

maximum

at

least

by

smaller

than

the

best

possible

label

for

O.

Thus

in

relaxing

constraints

for

equally

important

conceptual

views

an

adequate

algorithm

would

be

the

processing

of

decreasing

levels

of

quality

i.e.

finding

services

with

increasing

weightings

Starting

with

the

minimum

possible

relaxation

the

algorithm

will

always

work

over

an

entire

sequence

of

services

with

the

same

quality

index

and

return

all

the

discovered

services

of

the

lowest

level

found

together

with

their

quality

estimation

This

is

important

for

having

to

restart

the

algorithm

at

the

previous

point

of

termination

if

the

services

discovered

so

far

should

not

have

been

sufficient

and

the

evaluation

of

lesser

quality

levels

becomes

necessary

Similarly

knowing

the

labeling

technique

and

the

views

involved

a

user

can

also

specify

a

maximum

quality

value

up

to

which

he/she

is

willing

to

accept

more

general

services

For

our

algorithm

we

will

assume

a

declarative

query

mechanism

on

UDDI

directories

enhanced

by

all

the

feature

characteristics

described

by

their

respective

conceptual

views

like

pres

ented

in

-LSB-

-RSB-

However

symmetrically

relaxing

just

the

same

number

of

steps

even

when

assuming

views

of

the

same

granularity

and

importance

with

respect

to

the

user

query

will

generally

not

lead

to

our

desired

broad

relaxation

scheme

with

as

little

generalization

as

possible

Imagine

a

query

that

specifies

two

soft

predicates

of

which

one

is

a

leaf

node

of

a

view

whereas

the

other

predicate

specifies

a

direct

descendant

node

of

the

root

in

the

respective

view

If

no

perfect

match

should

be

given

our

strategy

would

result

in

relaxing

either

of

the

two

constraints

a

single

step

But

whereas

the

relaxation

by

a

single

step

from

our

leaf

node

usually

leads

to

a

slight

generalization

allowing

a

few

more

services

for

selection

the

relaxation

step

in

our

second

constraint

would

relax

to

the

root

node

and

thus

offer

the

total

number

of

services

registered

in

the

entire

ontology

for

the

respective

characteristic

i.e.

entirely

drop

the

second

constraint

Obviously

that

would

not

be

a

sensible

behavior

So

we

do

not

only

have

to

punish

deep

relaxation

steps

but

even

more

severely

refrain

from

relaxations

the

closer

we

are

to

the

root

This

concept

will

be

implemented

in

our

relaxation

algorithm

by

letting

the

longest

possible

relaxation

path

determine

the

weightings

for

all

constraints

-LRB-

again

assuming

a

comparable

level

of

detail

throughout

our

ontology

-RRB-

The

weightings

for

edges

along

the

relaxation

path

will

then

be

assigned

in

each

conceptual

view

in

descending

order

starting

from

the

root

down

to

the

concept

specified

by

the

query

Thus

the

relaxation

of

all

concepts

at

least

to

the

same

level

of

generalization

is

enforced

before

having

to

relax

already

more

general

concepts

In

our

example

from

above

the

relaxation

path

for

a

leaf

node

concept

would

be

assigned

weightings

as

given

by

the

height

of

the

conceptual

view

whereas

the

relaxation

path

for

a

concept

node

right

below

the

root

would

be

assigned

the

highest

possible

weighting

Thus

-LRB-

following

lemma

-RRB-

our

leaf

node

concept

will

have

been

relaxed

to

a

generalization

level

of

the

respective

concept

right

below

the

root

before

the

second

constraint

is

relaxed

for

the

first

time

Now

we

are

ready

to

present

an

algorithm

for

relaxation

of

symmetrical

constraints

incorporation

a

breadth

first

paradigm

and

a

minimum

level

of

generalization

strategy

Algorithm

Symmetrical

Constraints

Breadth

First

Paradigm

Pose

the

query

containing

only

the

hard

constraints

against

the

enhanced

UDDI

DAML-S

directory

If

an

empty

result

should

be

returned

terminate

the

algorithm

outputting

the

empty

result

set

Repose

the

query

with

all

soft

constraints

included

If

a

non-empty

result

should

be

returned

for

the

expanded

query

terminate

the

algorithm

and

output

the

respective

services

as

perfect

matches

with

a

relaxation

level

of

Given

n

the

number

of

soft

constraints

we

have

to

label

each

edge

along

the

relaxation

path

from

the

concept

specified

by

the

query

to

the

root

node

in

every

conceptual

view

-LRB-

cf.

lemma

-RRB-

Among

the

n

views

find

the

longest

possible

relaxation

path

and

set

maxdepth

as

the

maximum

depth

of

all

ontologies

relative

to

the

class

specified

in

the

service

request

For

every

view

label

the

relaxation

path

starting

from

the

root

by

n

d

down

to

the

concept

specified

by

the

query

-LRB-

d

-LRB-

maxdepth

-RRB-

to

descending

-RRB-

For

i

to

n

j

-LRB-

j

maxdepth

-RRB-

Start

with

the

query

including

all

hard

and

soft

constraints

and

build

statements

containing

any

possible

relaxation

with

a

weighting

of

i

i.e.

relax

in

turn

all

conceptual

views

by

one

step

up

to

the

point

of

reaching

the

desired

weighting

Due

to

construction

of

the

weighting

this

will

result

in

a

breadth

first

strategy

If

any

of

the

statements

produced

in

retrieves

a

non-empty

result

set

collect

results

of

all

possibilities

and

terminate

the

algorithm

In

this

algorithm

step

can

efficiently

be

implemented

using

an

A

Algorithm

that

successively

explores

the

differently

weighted

tree

edges

finding

all

possible

combinations

for

each

quality

weighting

However

please

note

that

not

every

weighting

is

possible

to

reach

by

relaxing

constraints

We

will

now

exemplify

the

above

algorithm

through

different

examples

consider

the

three

conceptual

views

X

Y

and

Z

given

in

figure

and

assume

a

user

has

posed

a

query

requesting

the

characteristics

and

as

soft

constraints

Let

us

assume

all

hard

filter

conditions

have

already

been

satisfied

but

the

basic

query

for

our

soft

constraints

fails

i.e.

no

service

with

these

capabilities

is

registered

Step

two

of

our

algorithm

will

now

label

the

relaxation

paths

like

shown

in

figure

-LRB-

bold

edges

and

shaded

vertexes

-RRB-

Starting

with

all

services

having

quality

values

of

one

concept

is

generalized

to

and

the

query

is

reposed

with

constraints

and

If

we

still

should

have

no

matching

services

we

have

to

go

on

relaxing

A

query

for

a

value

of

two

is

not

possible

but

for

a

value

of

we

can

relax

to

and

repose

the

query

with

constraints

and

Let

us

assume

we

still

have

not

found

a

result

the

next

quality

value

would

be

For

this

we

have

two

possibilities

and

have

to

unite

the

results

of

the

query

on

and

the

query

The

next

possible

quality

value

would

be

with

a

query

on

Please

note

that

we

indeed

have

relaxed

all

constraints

to

same

level

until

we

relax

any

constraint

to

the

top

level

-LRB-

e.g.

in

the

view

Y

-RRB-

for

the

first

time

The

algorithm

would

terminate

at

the

latest

after

relaxing

all

views

to

the

top

level

with

a

quality

value

of

Figure

Conceptual

views

with

labeled

relaxation

paths

Quantitative

Service

Quality

Measures

In

the

last

section

we

have

seen

an

effective

scheme

for

the

case

of

symmetrical

relaxations

under

the

assumption

of

equal

useful-ness

i.e.

one

broad

step

was

as

useful

as

any

other

broad

step

of

a

comparable

level

But

in

real

world

applications

query

constraints

are

not

always

only

of

a

qualitative

incomparable

nature

Deeper

knowledge

of

individual

user

's

preferences

or

knowledge

of

stereotypical

usage

can

become

interesting

parameters

in

assessing

the

quality

of

different

relaxation

schemes

Tuning

the

factor

to

a

certain

ratio

for

each

application

-LRB-

x

broad

steps

equal

deep

step

-RRB-

will

express

the

desired

semantics

in

each

instance

The

exact

coefficient

used

for

the

discrimination

of

deep

relaxation

in

each

instance

however

will

typically

strongly

depend

on

the

domain

the

total

number

of

soft

query

constraints

and

the

respective

granularity

of

the

views

ontology

-LRB-

i.e.

the

semantic

level

of

detail

-RRB-

used

Views

that

are

modeled

with

a

very

fine

granularity

can

be

relaxed

introducing

a

smaller

degree

of

generalization

to

the

service

request

results

than

would

be

introduced

by

those

views

that

are

modeled

rather

coarsely

anyway

Hence

a

deep

relaxation

step

in

a

very

detailed

ontology

might

be

worth

only

three

or

four

broad

relaxations

of

other

user

constraints

whereas

a

deep

step

in

a

coarser

ontology

used

within

the

same

query

might

add

up

to

the

worth

of

ten

broad

relaxation

steps

or

even

several

deep

relaxation

steps

of

other

constraints

Likewise

very

flat

hierarchies

with

many

subclasses

to

each

node

are

not

suited

too

well

for

deep

relaxation

So

the

discrimination

will

in

each

application

depend

on

The

relative

semantic

importance

of

a

view

with

respect

to

the

user

request

The

maximum

depth

of

each

conceptual

view

Its

total

number

of

-LRB-

sub

-RRB-

concepts

and

The

-LRB-

average

-RRB-

number

of

instances

in

each

concept

The

relative

semantic

importance

of

a

view

can

usually

only

be

determined

by

directly

consulting

the

user

But

in

the

following

we

will

give

an

overview

of

techniques

that

will

generally

help

to

deal

with

problems

of

relaxing

views

with

different

granularities

As

a

rule

of

thumb

we

can

state

that

the

relaxation

of

views

having

a

low

maximum

depth

and

rather

high

numbers

of

services

attached

to

each

node

should

be

delayed

as

long

as

possible

Our

technique

of

starting

the

view

graph

labeling

from

the

root

node

already

helps

facilitating

this

rule

If

a

shallow

conceptual

view

is

used

-LRB-

usually

an

indication

for

coarse

modeling

-RRB-

together

with

a

more

detailed

view

even

the

edges

to

leaf

nodes

in

the

shallow

ontology

will

be

assigned

rather

high

weightings

unlike

the

leaf

nodes

in

ontologies

with

a

rather

high

depth

This

behavior

is

however

not

always

the

best

choice

If

unlike

in

figure

the

respective

depths

of

conceptual

views

differ

by

a

considerable

amount

we

should

not

simply

delay

the

relaxation

of

shallow

views

but

have

to

insert

several

intermediate

steps

in

the

more

detailed

views

before

relaxing

the

next

step

in

a

coarse

view

Figure

shows

pairs

of

views

X

Y

and

X

Y

In

both

cases

the

depth

of

X

X

is

only

two

whereas

the

depth

of

Y

Y

is

four

That

means

that

in

terms

of

relaxation

we

can

assume

that

for

some

reason

the

more

shallow

ontologies

X

and

X

are

modeled

rather

coarsely

On

the

left

hand

side

in

figure

we

can

see

the

labeling

scheme

from

our

algorithm

Ontology

Y

would

be

relaxed

to

or

even

before

a

single

step

in

X

would

be

relaxed

On

the

right

hand

side

we

can

see

a

better

labeling

scheme

with

interleaved

relaxation

steps

-LRB-

two

steps

in

Y

for

a

step

in

X

-RRB-

Y

Y

Y

Y

Y

X

X

X

Figure

Differently

labeled

relaxation

paths

Since

the

interleaving

of

relaxation

steps

with

respect

to

the

maximum

depth

generally

seems

a

fairer

approach

we

will

incorporate

this

behavior

into

our

algorithm

If

the

maximum

depth

of

some

conceptual

views

should

severely

differ

we

will

assume

a

coarser

level

of

detail

and

find

out

how

many

steps

in

the

most

detailed

view

represent

a

single

step

in

the

coarser

view

We

then

re-label

the

coarse

view

beginning

from

the

root

by

adding

so

many

of

the

appropriate

sequence

of

weightings

as

steps

in

the

detailed

view

are

necessary

For

instance

in

figure

we

can

easily

see

that

a

single

step

in

X

-LRB-

depth

-RRB-

represents

two

steps

in

Y

-LRB-

depth

-RRB-

and

thus

we

would

have

to

re-label

the

first

edge

by

the

sum

of

the

two

highest

weights

of

Y

-LRB-

-RRB-

and

its

second

edge

by

the

sum

of

the

next

two

weights

-LRB-

-RRB-

Following

this

scheme

we

can

gain

the

more

suitable

relaxation

weights

of

view

X

In

terms

of

our

quality

assessment

algorithm

that

means

that

we

have

to

reconsider

the

labeling

of

the

relaxation

paths

in

step

two

and

replace

the

respective

section

by

the

following

Among

the

n

conceptual

views

find

the

longest

possible

relaxation

path

multiplying

possible

steps

in

each

view

relative

to

the

class

specified

in

the

service

request

by

the

view

's

respective

factor

of

q

where

q

is

given

as

the

integer

part

of

the

result

of

dividing

the

maximum

view

depth

by

the

maximum

depth

of

the

current

view

-LRB-

i.e.

q

steps

in

the

most

detailed

view

represent

one

step

in

this

view

-RRB-

Set

the

maximum

value

for

maxdepth

For

every

conceptual

view

label

the

relaxation

path

starting

from

the

root

by

n

d

n

n

d-q

-LRB-

i.e.

the

sum

of

the

first

q

weights

in

terms

of

the

most

detailed

relaxation

path

-RRB-

down

to

n

d

n

n

with

d

-LRB-

maxdepth

-RRB-

A

second

possibility

to

control

the

relaxation

properties

of

multiple

conceptual

views

is

the

incorporation

of

user

preferences

giving

a

preferred

relaxation

order

Incorporating

such

preferences

into

the

weights

along

the

ontology

however

is

a

very

difficult

problem

in

its

own

rights

and

therefore

beyond

the

scope

of

this

paper

For

the

case

that

a

simple

ordering

of

relaxation

for

the

views

is

given

by

the

user

we

can

use

double-labeled

relaxation

paths

like

for

the

tree

patterns

in

-LSB-

-RSB-

The

second

label

for

each

node

is

e.g.

the

respective

rank

of

the

view

in

a

specified

relaxation

ordering

or

the

number

of

the

node

's

respective

sub-concepts

In

the

case

that

for

the

execution

of

step

in

our

algorithm

more

than

one

query

should

be

possible

the

relaxations

can

then

be

executed

minimizing

the

sum

of

second

labels

and

retrieval

can

be

terminated

whenever

a

result

occurs

Also

for

the

case

that

a

prioritization

of

relaxations

is

give

n

-LRB-

cf.

-LSB-

-RSB-

-RRB-

the

respective

relaxation

scheme

is

straightforward

The

prioritized

view

is

successively

relaxed

until

a

first

result

set

is

retrieved

Then

the

second

third

etc.

views

are

used

to

break

ties

However

when

it

comes

to

integrating

weights

from

preferences

into

relaxation

weights

deeper

research

is

still

needed

While

in

the

above

discussions

we

assumed

the

existence

of

different

conceptual

views

to

a

given

ontology

the

actual

creation

of

these

views

is

beyond

the

scope

of

this

paper

Multiple

views

as

abstractions

of

data

sources

are

a

well

understand

concept

in

classical

databases

systems

Yet

the

concept

of

such

views

has

only

recently

been

addressed

in

the

context

of

the

Semantic

Web

through

the

proposal

of

the

view

definition

language

RVL

for

the

low

level

ontology

language

RDFS

-LSB-

-RSB-

RVL

uses

a

declarative

query

language

for

the

creation

of

virtual

schemas

which

in

turn

serve

as

views

on

existing

complex

ontologies

Please

note

that

although

we

merely

focused

on

tree-shaped

clippings

from

OWL

ontologies

as

simple

relaxation

hierarchies

in

our

examples

the

presented

concepts

are

general

enough

to

be

used

with

only

the

slightest

adaptations

together

with

other

types

of

ontologies

and

more

complex

views

e.g.

virtual

RVL

views

Choosing

the

right

Web

service

for

execution

has

been

considered

in

several

ways

For

legal

or

economical

points

of

view

especially

assurance

structures

guaranteeing

that

a

service

performs

the

desired

task

like

-LSB-

-RSB-

or

-LSB-

-RSB-

have

been

addressed

However

when

negotiating

about

execution

guarantees

or

costs

the

semantic

content

of

a

service

has

to

be

understood

and

its

specific

capabilities

have

already

to

be

agreed

on

Taking

a

more

user-centered

view

the

notion

of

services

reputation

for

subsequent

selection

-LSB-

-RSB-

or

the

quality

assessment

for

the

negotiation

of

service

level

agreements

-LSB-

-RSB-

have

been

proposed

However

these

approaches

focus

on

conceptual

designs

omitting

algorithms

how

to

assess

the

quality

in

each

instance

The

most

complete

framework

with

respect

to

heterogeneous

environments

featuring

multiple

ontologies

is

given

by

-LSB-

-RSB-

where

the

notion

of

information

loss

for

query

reformulation

is

defined

Unlike

our

work

presented

here

where

multiple

conceptual

views

of

a

service

ontology

occur

in

a

single

request

this

work

however

deals

with

the

loss

of

information

when

a

query

has

to

be

translated

from

one

into

another

ontology

-LRB-

e.g.

in

order

to

pose

it

to

a

different

data

source

-RRB-

Thus

it

is

rather

concerned

with

the

problem

of

ontology

mappings

The

area

of

service

request

relaxation

over

ontologies

also

shows

some

similarities

with

database

query

relaxation

frameworks

like

given

in

-LSB-

-RSB-

or

-LSB-

-RSB-

and

especially

recent

work

on

querying

semi-structured

data

like

in

XML

databases

In

the

case

of

XML

the

DTD

of

a

document

defines

its

structure

together

with

the

-LRB-

semantic

-RRB-

type

of

data

within

each

node

The

main

focus

of

querying

in

that

area

is

on

building

queries

without

perfect

knowledge

of

documents

structure

or

the

exact

data

it

contains

Exploiting

the

set

of

labels

given

by

a

XML

document

's

DTD

as

ontology

in

term

of

the

documents

structure

-LSB-

-RSB-

uses

the

result

sets

of

queries

to

define

the

semantic

equivalence

of

alternative

query

expressions

however

without

relaxing

concepts

within

queries

The

area

of

relaxation

for

tree-shaped

queries

not

only

on

a

structural

level

-LRB-

relax

to

any

descendant

node

instead

of

child

node

-RRB-

but

also

on

a

limited

semantic

level

-LRB-

find

author

of

document

instead

of

book

-RRB-

is

in

detail

addressed

in

-LSB-

-RSB-

Here

also

weightings

along

the

edges

of

trees

comparable

to

our

user

preference-driven

quality

assessments

in

section

are

discussed

Our

work

differs

mainly

in

that

we

can

rely

on

fine-granular

concept

ontologies

that

are

custom

made

by

domain

experts

and

used

in

central

service

provisioning

portals

or

UDDI

DAML-S

directories

Not

only

are

we

able

to

exploit

semantics

by

a

far

larger

extent

than

previous

work

but

we

also

provide

means

to

derive

sensible

weightings

for

edges

within

conceptual

views

based

on

general

user

preferences

and

a

fair

relaxation

paradigm

The

general

area

of

enhancement

of

UDDI

goes

back

to

describing

the

capabilities

of

Web

services

on

a

more

detailed

level

by

using

ontology

languages

like

DAML+OIL

-LSB-

-RSB-

An

example

for

the

efficient

mapping

of

DAML+OIL

capability

descriptions

onto

UDDI

records

is

given

in

-LSB-

-RSB-

Our

approach

for

result

quality

assessment

here

is

facilitated

by

the

database-based

approach

for

UDDI

enhancement

featured

in

-LSB-

-RSB-

Using

cooperative

database

technology

and

an

extended

declarative

query

language

like

the

one

given

in

-LSB-

-RSB-

this

framework

features

a

good

implementation

framework

for

our

quality

assessment

Queries

using

hard

and

soft

constraints

can

be

automatically

rewritten

using

the

relaxed

concepts

and

posed

against

a

database

of

service

descriptions

using

suitable

relaxation

ontologies

However

in

terms

of

quality

these

languages

feature

only

a

qualitative

result

set

under

the

notion

of

Pareto-optimality

Quantitative

quality

constraints

that

limit

the

flood

of

incomparable

results

delivered

by

the

exponential

growth

of

Pareto

sets

with

the

number

of

soft

constraints

in

the

request

are

not

considered

A

first

study

of

such

quality

measures

is

given

in

-LSB-

-RSB-

for

the

restricted

class

of

ceteris

paribus

preferences

like

discussed

in

section

In

this

paper

we

presented

a

framework

for

the

discovery

and

selection

of

Web

Services

based

on

personalized

quality

assessments

for

individual

service

users

Starting

with

a

set

of

conceptual

views

over

a

service

ontology

that

express

a

generalization

hierarchy

of

concepts

-LRB-

conceptual

views

-RRB-

for

different

services

capabilities

and

characteristics

we

proposed

to

enhance

today

's

UDDI

DAML-S

registries

by

a

matching

component

that

will

not

only

perform

a

filtering

of

services

according

to

user

specified

terms

but

also

allows

for

cooperative

matchmaking

between

service

descriptions

and

the

individual

user

's

preferences

Focusing

on

the

quality

assessment

component

of

such

an

enhancement

we

described

in

detail

how

to

deal

with

different

kinds

and

multiple

instances

of

conceptual

views

The

views

in

our

framework

contain

the

domain-specific

understanding

of

concepts

or

the

common

knowledge

that

users

typically

will

expect

when

trying

to

find

an

adequate

service

for

execution

If

no

service

that

offers

all

required

capabilities

can

be

found

relaxing

along

these

views

will

step

by

step

generalize

the

services

requested

features

until

a

best

possible

match

can

be

found

Thus

cooperative

behavior

can

be

introduced

for

improved

service

provisioning

We

focused

on

controlling

these

relaxation

steps

implementing

a

breadth

first

strategy

control

flow

to

delay

far-reaching

generalizations

for

as

long

as

possible

We

also

discussed

the

influence

of

relaxation

plans

for

various

views

which

may

differ

in

their

granularity

or

accuracy

of

discrimination

and

the

influence

of

individual

user

preferences

for

relaxation

orders

For

the

case

of

views

with

differing

level

of

details

we

gave

an

adequate

scheme

to

balance

the

control

flow

Nevertheless

the

exact

instantiation

of

the

views

and

the

adequate

weightings

chosen

still

will

usually

differ

between

application

areas

Anticipating

stereotype

interaction

patterns

or

experiences

of

past

interactions

however

the

service

provider

usually

is

able

to

also

provide

some

suitable

default

ontologies

for

cooperative

matchmaking

within

UDDI

DAML-S

registries

or

managed

service

portals

In

any

case

the

provisioning

of

suitable

facilities

for

the

assessment

of

Web

service

quality

can

be

expected

to

become

a

central

part

of

service

provisioning

and

will

essentially

influence

the

future

acceptance

of

Web

service

offers

by

individual

clients

In

this

paper

we

have

restricted

conceptual

views

to

tree-shaped

clippings

of

ontologies

with

view

elements

being

exclusively

related

through

is-a

relationships

-LRB-

stating

an

explicit

generalization

of

concepts

in

a

superclass/subclass

fashion

-RRB-

Of

course

also

other

relationships

within

ontologies

might

be

available

for

relaxation

tasks

in

service

requests

on

the

other

hand

their

semantic

meaning

will

usually

be

somewhat

more

difficult

Since

complex

ontologies

commonly

contain

named

relationships

between

entities

that

might

be

used

to

make

views

more

flexible

and

query

relaxation

more

meaningful

an

important

future

work

item

will

be

to

break

down

this

restriction

on

relationships

of

the

is-a

type

With

our

algorithms

focus

on

relaxation

paths

as

an

abstraction

of

the

underlying

views

our

general

framework

for

quality

assessment

can

be

expected

to

be

extensible

also

to

these

new

types

of

views

in

a

straightforward

manner

independently

of

the

exact

type

of

view

the

relaxation

path

was

derived

from

If

a

relaxation

of

a

constraint

along

a

certain

relationship

is

backed

by

sensible

relaxation

semantics

-LRB-

i.e.

is

meaningful

-RRB-

however

has

to

be

checked

in

each

individual

instance

Furthermore

our

future

work

will

focus

on

a

tighter

integration

of

individual

user

preferences

into

the

quality

assessment

process

like

addressed

in

section

Choosing

the

adequate

weightings

does

not

have

an

obvious

semantics

The

meaning

of

relaxing

one

constraint

is

two-times

better

than

relaxing

another

constraint

can

only

be

guessed

what

about

three-times

etc.

The

area

between

quantitative

quality

assessments

like

e.g.

re-weighting

techniques

or

relevance

feedback

as

known

from

the

area

of

IR

and

the

purely

qualitative

approaches

like

Pareto

optimality

of

solu

A

href

html

tions

like

given

in

-LSB-

-RSB-

offers

a

vast

variety

of

possibilities

to

explore

for

real

world

applications

Also

here

the

notion

of

stereotypical

usage

of

services

and

the

grouping

of

users

with

similar

intensions

might

lead

to

improved

service

provisioning

We

believe

that

using

and

extending

our

framework

is

a

vital

step

towards

getting

a

better

understanding

of

these

topics

In

any

case

assessing

quality

of

service

request

results

in

a

semantically

sensible

way

promises

to

pave

the

road

to

cooperative

provisioning

for

user-centered

services

We

would

like

to

thank

Achim

Leubner

and

Anthony

Tarlano

for

helpful

comments

and

suggestions

This

work

was

partially

funded

by

an

Emmy-Noether-Grant

of

the

German

Research

Foundation

-LRB-

DFG

-RRB-

-LSB-

-RSB-

S.

Amer-Yahia

S.

Cho

D.

Srivastava

Tree

Pattern

Relaxation

In

Proc

of

the

Int

Conf

on

Extending

Database

Technology

-LRB-

EDBT

-RRB-

Prague

Czech

Republic

-LSB-

-RSB-

A.

Ankolenkar

M.

Burstein

J.

Hobbs

et

al.

DAML-S

Web

Service

Description

for

the

Semantic

Web

In

Proc

of

the

Int

Semantic

Web

Conf

-LRB-

ISWC

-RRB-

Sardinia

Italy

LNCS

Springer

-LSB-

-RSB-

W.-T

Balke

A.

Badii

Assessing

Web

Services

Quality

for

Call-by-Call

Outsourcing

In

Proc

of

the

Int

Workshop

on

Web

Services

Quality

-LRB-

WQW

-RRB-

Rome

Italy

-LSB-

-RSB-

W.-T

Balke

M.

Wagner

Cooperative

Discovery

for

User-centered

Web

Service

Provisioning

In

Proceedings

of

the

First

International

Conference

on

Web

Services

-LRB-

ICWS

-RRB-

Las

Vegas

USA

-LSB-

-RSB-

W.-T

Balke

M.

Wagner

Towards

Personalized

Selection

of

Web

Services

In

Proceedings

of

the

International

World

Wide

Web

Conference

-LRB-

WWW

-RRB-

Alternate

Track

on

Web

Services

Budapest

Hungary

-LSB-

-RSB-

S.

Chaudhuri

Generalization

and

a

Framework

for

Query

Modification

In

Proc

of

the

Int

Conf

on

Data

Engineering

-LRB-

ICDE

-RRB-

Los

Angeles

USA

-LSB-

-RSB-

E.

Christensen

F.

Curbera

G.

Meredith

S.

Weerawarana

Web

Services

Description

Language

-LRB-

WSDL

-RRB-

-LSB-

-RSB-

D.

Connolly

et

al.

DAML+OIL

Reference

Description

Note

December

-LSB-

-RSB-

DAML

OWL-S

Semantic

Markup

for

Web

Services

-LSB-

-RSB-

DAML

OWL-S

Release

-LSB-

-RSB-

M.

Jakobsson

M.

Yung

On

Assurance

Structures

for

WWW

Commerce

In

Proc

of

Int

Conf

on

Financial

Cryptography

-LRB-

FC

-RRB-

Springer

LNCS

Anguilla

British

West

Indies

-LSB-

-RSB-

Y.

Kanza

Y.

Sagiv

Flexible

Queries

over

Semistructured

Data

In

Proc

of

the

ACM

Symp

on

Principles

of

Database

Systems

-LRB-

PODS

-RRB-

Santa

Barbara

USA

-LSB-

-RSB-

W.

Kie

ling

G.

K

stler

Preference

SQL

Design

Imple-mentation

Experiences

In

Proc

of

the

Int

Conf

on

Very

Large

Databases

-LRB-

VLDB

-RRB-

Hong

Kong

China

-LSB-

-RSB-

A.

Magkanaraki

V.

Tannen

V.

Christophides

D.

Plexousakis

Viewing

the

Semantic

Web

Through

RVL

Lenses

In

Proc

of

the

Int

Semantic

Web

Conf

-LRB-

ISWC

-RRB-

LNCS

Sanibel

Island

USA

-LSB-

-RSB-

E.

M.

Maximilien

M.Singh

Conceptual

Model

of

Web

Service

Reputation

In

SIGMOD

Records

-LRB-

-RRB-

-LSB-

-RSB-

M.

McGeachie

J.

Doyle

Efficient

Utility

Functions

for

Ce-teris

Paribus

Preferences

In

Proc

of

Conf

on

Artificial

Intelligence

and

Conf

on

Innovative

Applications

of

Artificial

Intelligence

-LRB-

AAAI/IAAI

-RRB-

Edmonton

Canada

-LSB-

-RSB-

G.

Medvinsky

C.

Lai

B.

Neuman

Endorsements

Licensing

and

Insurance

for

Distributed

System

Services

In

Proc

of

the

ACM

Conf

on

Computer

and

Communications

Security

Fairfax

USA

-LSB-

-RSB-

E.

Mena

V.

Kashyap

A.

Illarramendi

A.

Sheth

Imprecise

Answers

in

Distributed

Environments

Estimation

of

Information

Loss

for

Multi-Ontology

based

Query

Processing

In

International

Journal

of

Cooperative

Information

Systems

-LRB-

IJCIS

-RRB-

-LRB-

-RRB-

-LSB-

-RSB-

NTT

DoCoMo

home

page

-LSB-

-RSB-

M.

Paolucci

T.

Kawamura

T.

Payne

K.

Sycara

Importing

the

Semantic

Web

in

UDDI

In

Proc

of

the

Int

Workshop

on

Web

Services

e-Business

and

the

Semantic

Web

-LRB-

WES

-RRB-

Toronto

Canada

-LSB-

-RSB-

UDDI

The

UDDI

Technical

White

Paper

universal description discovery and integration

the generalization throughout ontologies

domain-specific understanding of concepts