Metrics-based Semantic Web Service Discovery, IEEE ICWS-2009

Exploiting Metrics for Semantic

Web Service Discovery- IEEE ICWS’09, Los Angeles, CA, USA, July 08, 2009 -

Stefan Dietze, Alessio Gugliotta, John Domingue

Knowledge Media Institute, The Open University, UK

� Semantic Web Services (SWS) mediation

� Two-fold representation approach for SWS

� Prototypical application

� Conclusions

Outline

sws:WebService

SWS.2

sws:WebService

SWS.3

sws:WebService

SWS.1

WebService

WS.2

WebService

WS.3

WebService

WS.1

Introduction Semantic Web Services

� Formal specifications of Web

services in terms of their

capabilities (Cap), interfaces (If)

and non-functional properties (Nfp)

� Capabilities describe assumptions

(Ass) and effects (Eff)

� Defined through ontologies O, i.e.

as tuple of concepts C, instances I,

properties P, relations R and

axioms A

� Reference models such as OWL-S,

WSMO, SAWSDL

� SWS discovery: matchmaking of

capabilities of SWS e.g. :

� SWS brokers match logical

expressions

(e.g. & )

Introduction

Semantic Mediation for SWS Discovery

432 IIAs ¬∩≡211 IIAs ∩¬≡

1212 EfEfAsAs ⊂∪⊂

sws:WebService

SWS.2

sws:WebService

SWS.3

sws:Request

R.1

sws:WebService

SWS.1

WebService

WS.2

WebService

WS.3

WebService

WS.1

? ?

� SWS discovery: matchmaking of

capabilities of SWS e.g. :

� SWS brokers match logical

expressions

(e.g. & )

� Heterogeneous SWS annotations

� Semantic mediation:

alignment/mapping of SWS

concepts/instances across distinct

SWS

Introduction

Semantic Mediation for SWS Discovery

432 IIAs ¬∩≡211 IIAs ∩¬≡

1212 EfEfAsAs ⊂∪⊂

sws:WebService

SWS.2

sws:WebService

SWS.3

sws:Request

R.1

sws:WebService

SWS.1

WebService

WS.2

WebService

WS.3

WebService

WS.1

Semantic-Level Mediation

Mediation between different semantic

representations

Semantic Mediation

Issues

SWS1

(get-flowers)

<Color rdf:ID="Lilac"/>

<Location rdf:ID="Milton_Keynes"/>

211 IIAs ∩¬≡

has-assumption

SWS Request (Consumer)

Semantic Mediation

Issues

SWS1

(get-flowers)



432 IIAs ¬∩≡211 IIAs ∩¬≡

SWS2

(provide-flowers)

<Colour rdf:ID="Purple"/>

<geospatialLocation rdf:ID="M-K"/>

has-assumption has-assumption

SWS (Provider)SWS Request (Consumer)

Semantic Mediation

Issues

SWS1

(get-flowers)



432 IIAs ¬∩≡211 IIAs ∩¬≡

SWS2

(provide-flowers)

<Colour rdf:ID="Purple"/>

<geospatialLocation rdf:ID="M-K"/>

has-assumption has-assumption

?

SWS (Provider)SWS Request (Consumer)

Issues:

� Heterogeneous SWS annotations

� Lack of implicit similarity information

(symbol grounding problem)

� Reliance on either:

� (i) manual mapping rules

� (ii) ontology mapping mechanisms

(exploiting linguistic or structural similarities)

� Costly and error-prone

=> representations which implicitly represent similarities across heterogeneous SWS ?

Semantic Mediation

Issues

� Refining SWS ontologies through multiple Conceptual Spaces (CS), i.e. multidimensional,

geometrical vector spaces

� Concept C in one SWS ontology O => Conceptual Space CS

� Instance I of C => member M (vector) in CS

Two-fold Approach

Refining SWS through Conceptual Spaces

Instance I1j Instance I1i

Concept C1x instance-of

refined-as-cs

refined-as-member refined-as-member

d1

d2

d3

instance-of

SWS Ontology O1

Conceptual Space CS1x

� Similarity-computation between SWS instances by means of spatial distances in CS

� Common agreement at schema (i.e. CS) level

� Facilitated through wide-spread use of upper-level ontologies

(DOLCE, SUMO…)

d2

Instance i2i Instance i1i

Conceptual Space CSx

Concept c1x

refined-as-cs

refined-as-member refined-as-member

Concept c2x

refined-as-cs

d1

d3

SWS Ontology O1 SWS Ontology O2

instance-of

Agent 1 Agent 2

instance-of

Two-fold Approach

Refining SWS through Conceptual Spaces

CS ontology enabling to represent SWS concepts / instances through CS:

� CS (refining concept C) represented through set of dimensions di each associated

with a prominence value pi: ,

� Assignment of measurement scales

� Dimension dj might be refined by further dimensions:

(CS possibly composed of subspaces),

Two-fold Approach

Formal CS Ontology

( ){ }PpCSddpdpdpCS iinn

n ∈∈= ,,...,, 2211

( ){ }DddpdpdpDd knn

n

j ∈== ''',...,'','' 2211

CS ontology enabling to represent SWS concepts / instances through CS:

� CS (refining concept C) represented through set of dimensions di each associated

with a prominence value pi: ,

� Assignment of measurement scales

� Dimension dj might be refined by further dimensions:

(CS possibly composed of subspaces),

� Members M (refining instances I) represented through vectors in CS:

� Similarity between two SWS instances (members) V and U calculated by means of

their Euclidean distance:

(where is the mean of a dataset U and su is the standard deviation from U)

Two-fold Approach

Formal CS Ontology

( ){ }PpCSddpdpdpCS iinn

n ∈∈= ,,...,, 2211

( ){ }DddpdpdpDd knn

n

j ∈== ''',...,'','' 2211

( ){ }MvvvvM in

n ∈= ,...,, 21

∑=

−−

−=

n

i v

i

u

ii

s

vv

s

uupvudist

1

2))()((),(

u

A Similarity-based Mediator

Implementation based on WSMO

� Implementation based on Web Service Modelling Ontology (WSMO) and SWS reasoning

environment IRS-III for similarity-based SWS discovery

wsmo:Mediator

Med.1

wsmo:WebService

SWS.2

wsmo:WebService

SWS.3

wsmo:Goal

G.1

wsmo:WebService

SWS.1

wsmo:MedWS

SWS.1.1 Comp. Sim.

(1)

(2)

(4)

(5)

(3)

A Similarity-based Mediator


� Implementation based on Web Service Modelling Ontology (WSMO) and SWS reasoning

environment IRS-III for similarity-based SWS discovery

� WSMO Mediator: computation of similarities between given request (WSMO Goal, G1) and

set of x associated SWS (SWS1..SWSx)

� Implemented through mediation Web service

wsmo:Mediator

Med.1

wsmo:WebService

SWS.2

wsmo:WebService

SWS.3

wsmo:Goal

G.1

wsmo:WebService

SWS.1

wsmo:MedWS

SWS.1.1 Comp. Sim.

(1)

(2)

(4)

(5)

(3)

� MedWS SWS1.1 computes x similarity values with Sim(G1,SWSj) defined as reciprocal to the

mean value of their individual member distances:

� With distk being the distance between one particular member vi of G1 and one member of

SWSj in the same CS

( )

1

11

)(

),(),(

−

=−

==∑

n

dist

SWSGDistSWSGSim

n

k

k

jiji

Measurement-based Service Selection


wsmo:Mediator

Med.1

wsmo:WebService

SWS.2

wsmo:WebService

SWS.3

wsmo:Goal

G.1

wsmo:WebService

SWS.1

wsmo:MedWS

SWS.1.1 Comp. Sim.

(1)

(2)

(4)

(5)

(3)

� Uses representational approach based on CS and similarity-based WSMO Mediator

� Aims at retrieval of distributed video resources

(provided within EU FP7 IP NoTube - http://notube.tv)

� Keyword-based searches across 5 (REST) Web services built on top of the following

(logical/physical) repositories

� BBC Backstage (world news feed) [ http://backstage.bbc.co.uk/ ]

� Open Video [ http://www.open-video.org/ ]

� YouTube (entertainment feed) [ http://www.youtube.com ]

� OU channel on YouTube [ http://www.youtube.com/ou ]

� YouTube (mobile feed) [ http://www.youtube.com/ou ]

� Similarity-based service discovery for a given request

SWS Mediation based on CS

Prototypical Application



SWS1:

OU-youtube

O1:Purp O1:Env

SWS2:

entertain-youtube

O2:Purp O2:Env

SWS3:

open-video

O3:Purp O3:Env

SWS4:

bbc-backstage

O4:Purp O4:Env

M62={v1, v2}

SWS5:

mobile-youtube

O5:Purp O5:Env

CS2 Environment SpaceCS1 Purpose Space

SWS6:

get-video-request

M61={v1, v2, v3}

WS1:

OU-youtube

WS2:entertain-youtube

WS3:

open-video

WS4:

bbc-backstage

WS5:

mobile-youtube

SWS1:

OU-youtube

O1:Purp O1:Env

SWS2:

entertain-youtube

O2:Purp O2:Env

SWS3:

open-video

O3:Purp O3:Env

SWS4:

bbc-backstage

O4:Purp O4:Env

M62={v1, v2}

SWS5:

mobile-youtube

O5:Purp O5:Env


SWS6:

get-video-request

M61={v1, v2, v3}

WS1:

OU-youtube


WS3:

open-video

WS4:

bbc-backstage

WS5:

mobile-youtube



{(p1*information, p2*education, p3*leisure)} = CS1 {(p4*resolution, p5*bandwidth)} = CS2



Assumption

)..()..( 2121 mSWSiSWSiSWSinSWSiSWSiSWSiSWSi EEEPPPAss ∪∪∪∪∪∪∪=

Members Pi in CS1 (purpose) Members Ej in CS2 (environment)

SWS1 P1(SWS1)={(0, 100, 0)} E1(SWS1)={(100, 100)}

SWS2 P1(SWS2)={(0, 0, 100)} E1(SWS2)={(100, 100)}

SWS3 P1(SWS3)={(50, 50, 0)} E1(SWS3)={(100, 100)}

SWS4 P1(SWS4)={(100, 0, 0)} E1(SWS4)={(100, 100)}

SWS5 P1(SWS5)={(100, 0, 0)} P2(SWS5)={(0, 100, 0)}

E1(SWS5)={(10, 10)}

� SWS annotated with Assumption Ass defined through conjunction of instances

(more expressive logical expressions to be considered)

� Instances refined through vectors (members)



Assumption

)..()..( 2121 mSWSiSWSiSWSinSWSiSWSiSWSiSWSi EEEPPPAss ∪∪∪∪∪∪∪=

Members Pi in CS1 (purpose) Members Ej in CS2 (environment)

SWS1 P1(SWS1)={(0, 100, 0)} E1(SWS1)={(100, 100)}

SWS2 P1(SWS2)={(0, 0, 100)} E1(SWS2)={(100, 100)}

SWS3 P1(SWS3)={(50, 50, 0)} E1(SWS3)={(100, 100)}

SWS4 P1(SWS4)={(100, 0, 0)} E1(SWS4)={(100, 100)}

SWS5 P1(SWS5)={(100, 0, 0)} P2(SWS5)={(0, 100, 0)}

E1(SWS5)={(10, 10)}

OU@Youtube SWS Educational purpose High resolution and bandwidth

� SWS annotated with Assumption Ass defined through conjunction of instances

(more expressive logical expressions to be considered)

� Instances refined through vectors (members)

SWS1:

OU-youtube

O1:Purp O1:Env

SWS2:

entertain-youtube

O2:Purp O2:Env

SWS3:

open-video

O3:Purp O3:Env

SWS4:

bbc-backstage

O4:Purp O4:Env

M62={v1, v2}

SWS5:

mobile-youtube

O5:Purp O5:Env


SWS6:

get-video-request

M61={v1, v2, v3}

WS1:

OU-youtube


WS3:

open-video

WS4:

bbc-backstage

WS5:

mobile-youtube



� Requests (WSMO Goals) defined through AJAX-based UI

� Requests consists of:

� Input parameters: set of keywords (free text)

� Assumption: defined through vectors

(measurements describing purpose and environment)

� Similarity-based discovery of most suitable service based on WSMO mediator

DEMO

SWS1:

OU-youtube

O1:Purp O1:Env

SWS2:

entertain-youtube

O2:Purp O2:Env

SWS3:

open-video

O3:Purp O3:Env

SWS4:

bbc-backstage

O4:Purp O4:Env

M62={v1, v2}

SWS5:

mobile-youtube

O5:Purp O5:Env


SWS6:

get-video-request

M61={v1, v2, v3}

WS1:

OU-youtube


WS3:

open-video

WS4:

bbc-backstage

WS5:

mobile-youtube

Some issues:

� SWS assumptions defined as conjunction of instances

=> consideration of arbitrary logical expressions required

� Additional representational effort

� Similarity-calculation requires overlapping CS and measurable quality dimensions

Conclusions

Discussion and Summary

Some issues:

� SWS assumptions defined as conjunction of instances

=> consideration of arbitrary logical expressions required

� Additional representational effort

� Similarity-calculation requires overlapping CS and measurable quality dimensions

…, however:

� Allows to compute similarities between distinct SWS

� Reduces the required level of common ontological agreement

(only required at schema level).

Conclusions

Discussion and Summary

Thank you!

E-mail: [email protected]

Web: http://people.kmi.open.ac.uk/dietze

Metrics-based Semantic Web Service Discovery, IEEE ICWS-2009

Technology

Transcript of Metrics-based Semantic Web Service Discovery, IEEE ICWS-2009