On Automating Web Services Discovery

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On Automating Web Services Discovery

• Boualem Benatallah, University of New South Wales
• Mohand-Said Hacid, Universite Lyon
• Alain Leger, France Telecom
• Christophe Rey, Universite Blaise Pascal
• Farouk Toumani, Universite Blaise Pascal

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Contents

• Introduction
• Description logic
• Example Travel reservations
• Semantic reasoning for Web services discovery
• Evaluation
• Future work
• Conclusions

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Purpose Service Discovery

• Effectively discovering services on the web
• Using description logics (DL)
• EU project that has been implemented

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Web Services

• Built on XML
• Standards
• SOAP (Simple Object Access Protocol)
• WSDL (Web Services Description Language)
• UDDI (Universal Description, Discovery, and
  Integration)
• Service description
• Service discovery (FOCUS)
• Communication

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Semantic web

• Purpose to improve technology to
• Organize
• Search
• Integrate
• Evolve Web-accessible resources
• Ontologies to identify metadata to
• Discover information sources
• Reason about their capabilities
• Give rich description and modeling of
• Services
• Properties
• Capabilities
• Behaviors

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Description Logic
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Definitions 1

• Terminology DL service descriptions
• Difference operation extract service
  description
• Reduced clause form and structure equivalence
• Structural subsumption identify unique
  difference
• Cover services that cover the query
• Rest and miss query services not found

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Definitions 2

• Best cover SOLUTION
• Hypergraph and transversal represent cover
• Hypergraph generation concept (vertex)
  service (edge)
• Cost of a set of vertices Low Cost search
• Profile cover services found
• Profile rest and profile miss services not
  found

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Theorem I The best covering problem is NP-hard

• Lemma 1 Characterization of the minimal rest or
  missing services is always unique
• Lemma 2 Characterization of covers that
  minimize the rest or the missing services is a
  minimal transversal of the hypergraph

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Theorem II Minimal transversal describes a
solution pair (xi, sj)

• Let Tr(H) xi, i 1..m be the set of minimal
• transversals for the hypergraph H and
• E sj, j 1..n an edge of H.
• Assume H H U E.
• Then we have
• xi U sj is a nonminimal transversal of H ?
  there exists a minimal transversal xk of H such
  that
• xk n E sj and
• xk \ sj is a subset of xi.

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Optimizations separate options of the algorithm

• PERS only minimal transversals are good
  candidates (reduces candidates for solution)
• BNB Branch and bound (prunes hypergraph)
• Preference to small cost
• Upperbound is Cost Evaluation

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Service Parameters - Example
SERVICE INPUT OUTPUT
ToTravel Itinerary Arrival TripReservation
FromTravel Itinerary Departure TripReservation
Hotel Destination StayDuration HotelReservation
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Tourism Ontology in DL
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Terminology (T)

• ToTravel ( 1 departurePlace) ? (V
  departurePlace.Location) ?
• ( 1 arrivalPlace) ? (V
  arrivalPlace.Location) ?
• ( 1 arrival-Date) ? (V
  arrivalDate.Date) ?
• ( 1 arrivalTime) ? (V
  arrivalTime.Time)
• FromTravel ( 1 departurePlace) ? (V
  departurePlace.Location)
• ? ( 1 arrivalPlace) ? (V
  arrivalPlace.Location) ?
• ( 1 departure-Date) ? (V
  departureDate.Date) ?
• ( 1 departureTime) ? (V
  departureTime.Time)
• Hotel Accommodation ?
• ( 1 destinationPlace) ? (V
  destinationPlace.Location) ?
• ( 1 checkIn) ? (V checkIn.Date) ?
• ( 1 checkOut) ? (V checkOut.Date) ?
• ( 1 nbAdults) ? (V nbAdults.Integer)
  ?
• ( 1 nbChildren) ? (V
  nbChildren.Integer)

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Query (Q)

• Q ( 1 departurePlace) ? (V departurePlace.Locat
  ion) ?
• ( 1 arrivalPlace) ? (V arrivalPlace.Locati
  on) ?
• ( 1 departureDate) ? (V
  departureDate.Date) ?
• Accommodation ?
• ( 1 destinationPlace) ? (V
  destinationPlace.Location)?
• ( 1 checkIn) ? (V checkIn.Date) ?
• ( 1 checkOut) ? (V check-Out.Date) ?
• carRental
• (carRental - NOT MATCHED)

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Vertices and Edges of the Hypergraph

• Vertices
• S VToTravel, VFromTravel, VHotel
• Edges
• G w(1departurePlace), w(V departurePlace.Locat
  ion),
• w(1arrivalPlace), w(V arrivalPlace.Locatio
  n),
• w(1departureDate), w(V departureDate.Date)
  ,
• wAccommodation,
• w(1destinationP lace), w(V destinationP
  lace.Location),
• w(1checkIn), w(V checkIn.Date),
• w(1checkOut), w(V checkOut.Date),
• wcarRental.

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Hypergraph HT Q (S,G)
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Generate the transversal hypergraph

• incremental subexponential time k O(log k)
• k input output

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Semantic reasoning for Web services discovery
DAML-S ontology

• ServiceProfile capabilities and parameters
• Description of the service (human-readable)
• Functional behavior transformation of inputs to
  outputs
• Nonfunctional attributes (i.e. cost)
• ServiceModel description of the operation
• ServiceGrounding how to access the service
  (messaging)

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Evaluation

• Prototype implementation of the computeBCov
  algorithm
• GOALS
• Validate the feasibility of the approach
• Test the correctness of the algorithm
• Study the performance and scalability of the
  algorithm (not completed)
• Implement in European project MKBEEM
• Multilingual Knowledge Based European
  Electronic Marketplace

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Algorithm to compute Best Cover
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Knowledge Representation - MKBEEM Multilingual
Knowledge Based European Electronic Marketplace
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Configurations of test cases
Configurations Case 1 Case 2 Case 3
Number of defined concepts in the application domain ontology 365 1334 3405
Number of Web services 366 660 570
Number of atomic clauses in the query 6 33 12
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Execution Time
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Future work

• Extend the proposed framework to include
• Best covering problem where the difference
  operation is not semantically unique
• Service discovery with a large number of
  hetergeneous ontologies
• Support for service composition automation

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Conclusions

• Good example of applying description logic to a
  web service discovery problem
• Only works when there is a best covering problem
  that is semantically unique
• Removes solutions that do not cover minimum
  services (BEST solution may be a combination)
• IF all services cannot be provided by one service
  provider (i.e. car rental), how is the query
  divided
• IF there are millions of service providers
  instead of just hundreds, how do you manage