Semantic Web Service Discovery: Methods, Algorithms and Tools

fizzlargeSecurity

Nov 3, 2013 (3 years and 11 months ago)

103 views

Cardoso J. "Semantic Web: Theory, Tools and Applications"

Semantic Web Service Discovery:
Methods, Algorithms and Tools

Chapter 11

Do not put anything here.

This area is reserved

for the book cover.

Cardoso J. "Semantic Web: Theory, Tools and Applications"

2

Chapter Outline


Introduction


Web Services



Semantic Web Services


Web Service Discovery


Semantic Web Service Discovery


Architectures


Methods/algorithms


Tools


Open Issues

Cardoso J. "Semantic Web: Theory, Tools and Applications"

3

Web Services (WS)



programmatic interfaces for applications (i.e.,
business logic), available over the WWW
infrastructure and developed with XML
technologies
.


Cardoso J. "Semantic Web: Theory, Tools and Applications"

4

Semantic Web Services (SWS) I


Semantic Web (SW)
[
Antoniou, 2004]


Ontologies


Rules


Languages (e.g., OWL, RDF)


SW + WS = SWS


Web services annotated with semantics


Annotation includes:


Service description, provider details, service
operations, service execution model, service
parameters, service data flow, service
invocation details, …


Cardoso J. "Semantic Web: Theory, Tools and Applications"

5

Semantic Web Services II


The annotation terms adhere to
formal terminologies, a.k.a.
ontologies


Service
-
related SW technologies


DAML
-
S, OWL
-
S, WSDL
-
S, SWSO/SWSL,
WSMO/WSML
[Cardoso, 2005]



Cardoso J. "Semantic Web: Theory, Tools and Applications"

6

Chapter Outline


Introduction


Web Services



Semantic Web Services


Web Service Discovery


Semantic Web Service Discovery


Architectures


Methods/algorithms


Tools


Open Issues


Cardoso J. "Semantic Web: Theory, Tools and Applications"

7

WS Reference Architecture

Cardoso J. "Semantic Web: Theory, Tools and Applications"

8

Architectural Components


Service Registry


“yellow pages” for services


Matching Algorithm


Implemented in Matching Engine


Affects discovery effectiveness


Service Request


Captures requestor’s
information need


Service Advertisement


Describes a service


Created by service provider


Assumption
:

Identical
format

Cardoso J. "Semantic Web: Theory, Tools and Applications"

9

WS Description


WSDL


XML language for textual service
description


UDDI


Data model and API for service
publication/searching


Contains links to WSDL documents


Main elements:


businessEntity, businessService,
bindingTemplate, tModel

Cardoso J. "Semantic Web: Theory, Tools and Applications"

10

WS Matchmaking


Standard UDDI


Keyword
-

and category
-
based search


“Find qualifiers” (e.g., wildcards)


Manual (Web browsing) or through API


Information Retrieval (IR) techniques


similarity measures, clustering, etc.



Cardoso J. "Semantic Web: Theory, Tools and Applications"

11

Pitfalls of WS Discovery (1)


Informal description of service
functionality/capabilities


Unstructured, natural language descriptions


NAICS: Category


Dating Services

does not match

Personal Relationships Services



Incomplete description of service
functionality/capabilities


Providers are not obliged to provide complete service
info


Syntactic relevance vs. intentional relevance


Linguistic polysemy and ambiguity are problems


Keywords cannot capture operational service
semantics, useful during discovery/composition

Cardoso J. "Semantic Web: Theory, Tools and Applications"

12

Pitfalls of WS Discovery (2)


Lack of constraint specifications


Preconditions and other constraints are
useful for the entire service lifecycle


Limited expressiveness of domain
classification schemes


E.g.,
NAICS
,
UNSPSC


No support for indirect matching


UDDI does not support even simple
compositions


Cardoso J. "Semantic Web: Theory, Tools and Applications"

13

Chapter Outline


Introduction


Web Services



Semantic Web Services


Web Service Discovery


Semantic Web Service Discovery


Architectures


Methods/algorithms


Tools


Open Issues

Cardoso J. "Semantic Web: Theory, Tools and Applications"

14

New Architectural Components (1)


Service Annotation Ontologies (SAO)


Formal service description models


Specify service capabilities


OWL
-
S, WSMO, WSDL
-
S, SWSO


Domain Ontologies


Domain
-
specific terminologies


Substitute keywords and free text in service
descriptions


Hierarchies of concepts and relationships


Written in OWL, DAML+OIL, RDF(S), …





Cardoso J. "Semantic Web: Theory, Tools and Applications"

15

Example: The OWL
-
S SAO


Service Profile
[
Martin, 2005]



Human
-
readable service description and
provider’s contact details


Functional parameters


Inputs, Outputs, Preconditions, Effects


Non
-
functional parameters (e.g., QoS)


Mostly used in service discovery


Service Model


Control and data flow of service execution


Service Grounding


Service access and invocation details


Link to WSDL description




Cardoso J. "Semantic Web: Theory, Tools and Applications"

16

Example: A Beer domain ontology

http://www.dayf.de/2004/owl/beer_v0.3.owl


Cardoso J. "Semantic Web: Theory, Tools and Applications"

17

Revised “Traditional” Components


Service Registry


UDDI is still used but with references to semantic
descriptions


Matching Algorithm


More complex and “intelligent”


Exploits the formal semantics of service descriptions


Service Advertisement


Written in a SAO


Refers to concepts of a domain ontology


Service Request


Usually similar to an advertisement


Ontology integration and semantic mediation can be
applied to bridge different request
-
advertisement
specifications


Cardoso J. "Semantic Web: Theory, Tools and Applications"

18

Centralized Architecture I

Semantic extension of UDDI


tModels point to semantic
descriptions


Translator creates such
semantic tModels


Semantic matching is
performed in an external
engine


Keyword
-
based matching can
still be used


Some extensions to UDDI
Inquiry API are needed



Cardoso J. "Semantic Web: Theory, Tools and Applications"

19

Centralized Architecture II

The matching algorithms
themselves are published as WS


Support for diverse SAOs and
matching algorithms


Step1:

Ad hoc
selection of the
best matching service


Step2: Invocation of selected
service with the request as
parameter


Requires minor UDDI API changes


Allows more flexible business
models but complicates service
composition

Cardoso J. "Semantic Web: Theory, Tools and Applications"

20

Peer
-
to
-
Peer Architecture

P2P suitable (i.e., scalable, efficient) for distributed environments (e.g.,
Web)

Peers may be service requestors or providers


Each peer
-
requestor
may use its own
matching algorithm


Each peer
-
provider
can directly update
the local service
advertisements


Result: high flexibility


Cardoso J. "Semantic Web: Theory, Tools and Applications"

21

Chapter Outline


Introduction


Web Services



Semantic Web Services


Web Service Discovery


Semantic Web Service Discovery


Architectures


Methods/algorithms


Tools


Open Issues

Cardoso J. "Semantic Web: Theory, Tools and Applications"

22

Degree of Match (DoM)


A

value that expresses how similar
two entities are, with respect to some
similarity metric(s)


Important feature of most SWS
matchmaking approaches


Allows for ranking of discovered
services


Example DoM set: exact, plugin,
subsumes, subsumed
-
by, fail


Cardoso J. "Semantic Web: Theory, Tools and Applications"

23

Variety of Matchmaking Approaches


Direct


Return only single services that match the
request


Indirect


Compute service compositions (or “chains” in
the simplest case)


Logic
-
based


Description Logics and First Order Logic
reasoning


Similarity
-
based (IR techniques)


Linguistic similarity, term frequency, …


Graph matching

Cardoso J. "Semantic Web: Theory, Tools and Applications"

24

Approach I


Semantic Capabilities
Matching


A pioneering work
[Paolucci, 2002a]


Main idea


An advertisement A matches a request R when all the
outputs of R are matched by the outputs of A, and all
the inputs of A are matched by the inputs of R


DL subsumption matching between inputs and outputs


Outputs
are regarded more significant than
inputs





Degree of Match

Matching conditions

EXACT

If req.o is
equivalent

to adv.o, or

If req.o is a direct subclass of adv.o

PLUGIN

If adv.o
subsumes

req.o

SUBSUMES

If req.o
subsumes

adv.o

FAIL

If there is no subsumption relationship between
req.o and adv.o

The
inverse
conditions
hold for
inputs

Cardoso J. "Semantic Web: Theory, Tools and Applications"

25

Approach II


Multi
-
level Matching


A variant of Approach I


Main idea


Both functional and non
-
functional service data
matters


Multi
-
level matching


IOPE attributes, service categories, custom
service parameters (e.g., QoS
-
related)


DoM aggregation


Weighting the DoM of the various levels


A very difficult optimization problem



Cardoso J. "Semantic Web: Theory, Tools and Applications"

26

Approach III


DL Matchmaking
with Service Profile Ontologies


Service Profile Ontology


Concepts are DL expressions of service
constraints


DL reasoners create the ontology tree


A logic
-
based service registry


DL subsumption matching


The DoM set of Approach I is re
-
defined


A new DoM is introduced
[Li, 2004]


An advertisement matches a request if
their intersection is satisfiable

Cardoso J. "Semantic Web: Theory, Tools and Applications"

27

Approach III
-

Example

2 Advertisements
and a Request Q

The Service Profile Ontology after DL reasoning

DoM(Q,FreeDatingService) =
PLUGIN

DoM(Q,FreeDatingServiceForMovie…) =
SUBSUME


*Assumption: PLUGIN is better than SUBSUME

Cardoso J. "Semantic Web: Theory, Tools and Applications"

28

Approach IV


Similarity Measures and
Information Retrieval Techniques


Pure Logic
-
based matching may have
counterintuitive results. Example:


R input: InterestProfile



hasInterest.SciFiMovies


R output: ContactProfile


A input: InterestProfile


A output:
ChatID


DoM(R,A) =

FAIL


Reason: output of R is

disjoint with
output of A

although their inputs are

“logically relevant”

PersonalProfile

InterestProfile

ChatID

ContactProfile

is
-
a

disjoint
-
with

Cardoso J. "Semantic Web: Theory, Tools and Applications"

29

Approach IV


Similarity Measures and
Information Retrieval Techniques


Solution


Main idea


Allow for more “flexible” methods of assessing
service similarity


IR and similarity
-
based methods are perfect
candidates


E.g., linguistic semantics (WordNet similarity),
TF
-
IDF


Logic is just one component of
“relevance”


Such methods capture some other components


A problem remains


How much should each method contribute to the
DoM calculation


An optimization problem

Cardoso J. "Semantic Web: Theory, Tools and Applications"

30

Approach V


A Graph
-
based Approach


A service is represented as a DAG


Nodes ~ individuals of concepts


Arcs ~ roles between individuals


Main idea


Structural match
:
Two service descriptions match if they have the
same structure and the corresponding nodes match


Existing graph matching algorithms apply


No (obvious) support for DoM

Cardoso J. "Semantic Web: Theory, Tools and Applications"

31

Approach VI


Indirect Graph
-
based Matching


Indirect matching


Complex workflow compositions


“Service chains” in the simplest case


Service chain creation rules

1) The inputs of each involved service match
either the request inputs or the outputs of
the previous service in the chain.

2) Each output of the request is matched
against an output of the last service in the
chain.

Cardoso J. "Semantic Web: Theory, Tools and Applications"

32

Approach VI
-

Example

Service

Inputs

Outputs

S1

A, B

E

S2

A, B, C

F, N

S3

E, C

F

S4

F

K, M

S5

K, D

Z, Y

S6

K

D, Z

S7

D

Y

Discovered Service


Chains


S1, S3, S4, S6, S7

S1, S3, S4, S5

S2, S4, S6, S7

S2, S4, S5

Request inputs:{A,B,C,D}

Request outputs:{Z,Y}

3:

1: Service

specifications

2: Service

graph

Policy
-
based

service chain

selection can be applied

(e.g., the shortest)

S1

S2

S3

S4

S5

S6

S7

Cardoso J. "Semantic Web: Theory, Tools and Applications"

33

Approach VII


Indirect Backward
Chaining Matching


A similar approach for discovery of complex
service workflows… but implemented
through logic resolution


Main idea:
backward
-
chaining


goal
-
driven reasoning procedure


starting from services that match the request
outputs (but not its inputs), we recursively try to
link them with other services until we find a
service with all its inputs matched to the inputs
given by the request


Inherent support by logic programming
tools (Prolog)

Cardoso J. "Semantic Web: Theory, Tools and Applications"

34

Synopsis of Approaches

Characteristics

Approach

Matching elements

Support for DoM

Indirect matching

Algorithm

I

IO

Yes

No

Logic

II

IO, service category,

custom parameters

Yes

No

Logic

III

Service profile

Yes

No

Logic

IV

Textual descriptions,
IOPE

Yes

No

Logic+
Similarity

V

Service profile

No

No

Logic+
graphs

VI

IO

No

Yes

Hybrid+
graphs

VII

IO

No

Yes

Logic

Cardoso J. "Semantic Web: Theory, Tools and Applications"

35

Chapter Outline


Introduction


Web Services



Semantic Web Services


Web Service Discovery


Semantic Web Service Discovery


Architectures


Methods/algorithms


Tools


Open Issues

Cardoso J. "Semantic Web: Theory, Tools and Applications"

36

OWL
-
S/UDDI Matchmaker
(OWL
-
S/UDDIM)


OWL
-
S services


OWL domain ontologies


DL subsumption
-
based matchmaking


Standalone and Web
-
based versions


Standalone version has a client API


Open source (Java)


Intelligent Software Agents Group
,

Carnegie Mellon University


http://projects.semwebcentral.org/projects
/owl
-
s
-
uddi
-
mm/


Cardoso J. "Semantic Web: Theory, Tools and Applications"

37

IBM Semantic Tools for Web
Services (STWS)


WSDL
-
S services


OWL domain ontologies


Applies AI planning techniques to find
composite services that match the
request


Eclipse plug
-
in


Exploits the WordNet lexicon


http://www.alphaworks.ibm.com/tech
/wssem


Cardoso J. "Semantic Web: Theory, Tools and Applications"

38

Hybrid OWL
-
S Web Service
Matchmaker (OWLS
-
MX)


OWL
-
S services


OWL domain ontologies


Logic
-
based matching + syntactic token
-
based similarity metrics


A service test collection is also available


Open source (Java)


German Research Center for Artificial
Intelligence, DFKI Saarbruecken



http://www.dfki.de/~klusch/owls
-
mx/


Cardoso J. "Semantic Web: Theory, Tools and Applications"

39

METEOR
-
S Web Service Discovery
Infrastructure (MWSDI)
-

Lumina


WSDL
-
S services


OWL domain ontologies


Adds semantic to the whole service lifecycle


METEOR
-
S discovery API used by the
graphical tool Lumina (Eclipse plug
-
in)


Open source (Java)


Large Scale Distributed Information
Systems (LSDIS) Lab, University of Georgia



http://lsdis.cs.uga.edu/projects/meteor
-
s/illumina/

Cardoso J. "Semantic Web: Theory, Tools and Applications"

40

TUB OWL
-
S Matcher (OWLSM)


OWL
-
S services


OWL domain ontologies


DL subsumption
-
based weighted matching
over many service parameters


Open source (Java)


Technical University of Berlin


http://kbs.cs.tu
-
berlin.de/ivs/Projekte/owlsmatcher/index.ht
ml

Cardoso J. "Semantic Web: Theory, Tools and Applications"

41

WSMX Discovery Component


WSMO services


WSML domain ontologies


Part of the WSMO reference
implementation


Open source (Java)


WSMX working group, European
Semantic Systems cluster initiative


http://www.wsmx.org/

Cardoso J. "Semantic Web: Theory, Tools and Applications"

42

Chapter Outline


Introduction


Web Services



Semantic Web Services


Web Service Discovery


Semantic Web Service Discovery


Architectures


Methods/algorithms


Tools


Open Issues

Cardoso J. "Semantic Web: Theory, Tools and Applications"

43

Evaluation of Discovery


Evaluation of efficiency (e.g., scalability,
service retrieval times) is not enough


Retrieval effectiveness must be assessed


Several obstacles exist


Lack of SWS test sets and evaluation testbeds


OWL
-
S Test Collection (TC) is a good start
[Klusch, 2005]


Lack of appropriate evaluation metrics


Standard IR metrics (precision, recall) may not
apply as
-
is

Cardoso J. "Semantic Web: Theory, Tools and Applications"

44

Semantic Interoperability/Mediation


In practice, service requestors and service
providers will use different SAO and/or
domain ontologies


A mediation layer will be necessary


Provision of ontology matching and alignment


Translation from natural language requests to
formal ontology
-
based


WSMO discovery heavily relies on
mediators
[
Roman, 2005]

Cardoso J. "Semantic Web: Theory, Tools and Applications"

45

Maturity of Discovery Tools/Engines


Tools are not limited to discovery
frameworks, but also include:


Registries


Annotation tools


Service editors


No stable, fully
-
documented tools
currently exist


Interoperability between research
efforts is a major issue



Cardoso J. "Semantic Web: Theory, Tools and Applications"

46

Fuzziness in Discovery


Soft Computing concepts may give added
value to SWS discovery through
approximate matching


Human information needs may not be
completely represented by ontologies which
are rather crisp KR tools


Even reasoning over concrete domains may
be insufficient in practice


Researchers are already pursue
fuzzification of ontologies and matchmaking



Cardoso J. "Semantic Web: Theory, Tools and Applications"

47

Conclusion


SWS provide new opportunities for
effective service discovery


Most existing solutions exploit DL
reasoning services


IR and knowledge discovery techniques
seem to be applicable


There are interesting tools but only at a
research
-
level


However, many open issues still exist

Cardoso J. "Semantic Web: Theory, Tools and Applications"

48

Conclusion


See
Appendix I

for a mini
-
tutorial
on a SWS discovery tool


See
Appendix II

for a DL primer


p
-
comp web site

http://p
-
comp.di.uoa.gr