Transcript On Automating Web Services Discovery

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
Contents
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Introduction
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Description logic
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Example – Travel reservations
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Semantic reasoning for Web services discovery
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Evaluation
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Future work
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Conclusions
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Purpose – Service Discovery
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Effectively discovering services on the web
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Using description logics (DL)
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EU project that has been implemented
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Web Services
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Built on XML
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Standards:
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SOAP (Simple Object Access Protocol)
WSDL (Web Services Description Language)
UDDI (Universal Description, Discovery, and Integration)
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Service description
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Service discovery (FOCUS)
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Communication
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Semantic web
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Purpose to improve technology to:
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Organize
Search
Integrate
Evolve Web-accessible resources
Ontologies to identify metadata to:
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Discover information sources
Reason about their capabilities
Give rich description and modeling of
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Services
Properties
Capabilities
Behaviors
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Description Logic
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Definitions 1
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Terminology – DL service descriptions
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Difference operation – extract service description
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Reduced clause form and structure equivalence
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Structural subsumption – identify unique difference
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Cover – services that cover the query
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Rest and miss – query services not found
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Definitions 2
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Best cover – SOLUTION
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Hypergraph and transversal – represent cover
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Hypergraph generation – concept (vertex) &
service (edge)
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Cost of a set of vertices – Low Cost search
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Profile cover – services found
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Profile rest and profile miss – services not found
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Theorem I – The best covering problem is
NP-hard
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Lemma 1 – Characterization of the minimal
rest or missing services is always unique
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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 ∩ E = {sj} and
xk \ {sj} is a subset of xi.
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Optimizations – separate options of the
algorithm
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PERS – only minimal transversals are good
candidates (reduces candidates for solution)
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BNB – Branch and bound (prunes
hypergraph)
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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.Location) ∏
(≥ 1 arrivalPlace) ∏ (V arrivalPlace.Location) ∏
(≥ 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
Σ = {VToTravel, VFromTravel, VHotel}
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Edges
Γ = {w(≥1departurePlace), w(V departurePlace.Location),
w(≥1arrivalPlace), w(V arrivalPlace.Location),
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 = (Σ,Γ)
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Generate the transversal hypergraph
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incremental subexponential time k O(log k)
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k = input# + output#
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Semantic reasoning for Web services
discovery – DAML-S ontology
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ServiceProfile – capabilities and parameters
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Description of the service (human-readable)
Functional behavior – transformation of inputs to outputs
Nonfunctional attributes (i.e. cost)
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ServiceModel – description of the operation
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ServiceGrounding – how to access the service
(messaging)
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Evaluation
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Prototype implementation of the computeBCov
algorithm
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GOALS
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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
Number of Web services
365
1334
3405
366
660
570
Number of atomic clauses
in the query
6
33
12
23
Execution Time
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Future work
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Extend the proposed framework to include:
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Best covering problem where the difference
operation is not semantically unique
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Service discovery with a large number of
hetergeneous ontologies
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Support for service composition automation
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Conclusions
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Good example of applying description logic to a web
service discovery problem
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Only works when there is a best covering problem
that is semantically unique
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Removes solutions that do not cover minimum
services (BEST solution may be a combination)
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IF all services cannot be provided by one service
provider (i.e. car rental), how is the query divided
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IF there are millions of service providers instead of
just hundreds, how do you manage
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