Cardoso J. "Semantic Web: Theory, Tools and Applications"
Semantic Web Service Discovery: Methods, Algorithms and Tools
Chapter 11
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Chapter Outline Introduction
Web Services Semantic Web Services
Web Service Discovery Semantic Web Service Discovery
Architectures Methods/algorithms Tools Open Issues
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Web Services (WS) programmatic interfaces for applications (i.e.,
business logic), available over the WWW infrastructure and developed with XML technologies.
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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, …
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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]
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Chapter Outline Introduction
Web Services Semantic Web Services
Web Service Discovery Semantic Web Service Discovery
Architectures Methods/algorithms Tools Open Issues
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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
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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
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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.
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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
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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
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Chapter Outline Introduction
Web Services Semantic Web Services
Web Service Discovery Semantic Web Service Discovery
Architectures Methods/algorithms Tools Open Issues
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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), …
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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
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Example: A Beer domain ontology
http://www.dayf.de/2004/owl/beer_v0.3.owl
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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
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Centralized Architecture ISemantic 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
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Centralized Architecture IIThe 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
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Peer-to-Peer ArchitectureP2P 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
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Chapter Outline Introduction
Web Services Semantic Web Services
Web Service Discovery Semantic Web Service Discovery
Architectures Methods/algorithms Tools Open Issues
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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
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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
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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
EXACTIf req.o is equivalent to adv.o, orIf req.o is a direct subclass of adv.o
PLUGIN If adv.o subsumes req.o
SUBSUMES If req.o subsumes adv.o
FAILIf there is no subsumption relationship between req.o and adv.o
The inverse conditions hold for inputs
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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
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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
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Approach III - Example
2 Advertisements and a Request Q
The Service Profile Ontology after DL reasoning
DoM(Q,FreeDatingService) = PLUGINDoM(Q,FreeDatingServiceForMovie…) = SUBSUME
*Assumption: PLUGIN is better than SUBSUME
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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 Aalthough their inputs are “logically relevant”
PersonalProfilePersonalProfile
InterestProfileInterestProfileChatIDChatIDContactProfileContactProfile
is-a
disjoint-with
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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
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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
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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.
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Approach VI - ExampleService 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, S7S1, S3, S4, S5S2, S4, S6, S7
S2, S4, S5
Request inputs:{A,B,C,D}Request outputs:{Z,Y}
3:
1: Service specifications
2: Servicegraph
Policy-based service chain selection can be applied
(e.g., the shortest)
S1S1
S2S2
S3S3
S4S4
S5S5
S6S6 S7S7
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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)
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Synopsis of ApproachesCharacteristics
Approach Matching elements Support for DoM Indirect matching Algorithm
I IO Yes No Logic
IIIO, service category,custom parameters
Yes No Logic
III Service profile Yes No Logic
IVTextual descriptions,
IOPEYes No
Logic+ Similarity
V Service profile No NoLogic+ graphs
VI IO No YesHybrid+ graphs
VII IO No Yes Logic
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Chapter Outline Introduction
Web Services Semantic Web Services
Web Service Discovery Semantic Web Service Discovery
Architectures Methods/algorithms Tools Open Issues
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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/
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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
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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/
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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/illu
mina/
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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/owlsm
atcher/index.html
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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/
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Chapter Outline Introduction
Web Services Semantic Web Services
Web Service Discovery Semantic Web Service Discovery
Architectures Methods/algorithms Tools Open Issues
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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
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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]
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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
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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
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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