Semantic Web Service Discovery: Methods, Algorithms and Tools

1
Semantic Web Service Discovery Methods,
Algorithms and Tools

• Chapter 11

Do not put anything here. This area is reserved
for the book cover.
2
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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WS Reference Architecture
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Architectural Components

• Service Registry
• yellow pages for services
• Matching Algorithm
• Implemented in Matching Engine
• Affects discovery effectiveness
• Service Request
• Captures requestors 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/searchi
  ng
• 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, DAMLOIL, RDF(S),

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Example The OWL-S SAO

• Service Profile Martin, 2005
• Human-readable service description and providers
  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-advertiseme
  nt specifications

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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

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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

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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

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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

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) PLUGIN DoM(Q,FreeDating
ServiceForMovie) 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.SciFiMovie
  s
• R output ContactProfile
• A input InterestProfile
• A output ChatID

PersonalProfile
DoM(R,A) FAIL Reason output of R is
disjoint with output of A although their inputs
are logically relevant
is-a
InterestProfile
ChatID
ContactProfile
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 - Example
1 Service specifications
Discovered Service Chains S1, S3, S4, S6,
S7 S1, S3, S4, S5 S2, S4, S6, S7 S2, S4, S5
S1
S3
S5
2 Service graph
S2
S4
S6
S7
Policy-based service chain selection can be
applied (e.g., the shortest)
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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 Approaches
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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-u
  ddi-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/illumina
  /

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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/owlsmatche
  r/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

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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