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Forschungszentrum Informatik, Karlsruhe
FZI FZI Research Center for Information ScienceResearch Center for Information Scienceat the University of Karlsruheat the University of Karlsruhe
Variance in e-Business
Service Discovery
Stephan Grimm,
Boris Motik,
Chris Preist (HP Labs, Bristol)
Slide 2
OverviewOverview
• Introduction
• Intuition behind modelling service semantics
• Operationalising discovery using logic
• Matching service descriptions
• Conclusion
Slide 3
Service Discovery in the Semantic WebService Discovery in the Semantic Web
• Service– Web Service vs. high-level eBusiness Service
• Service Discovery– Locating Providers who meet a Requestor´s needs
– Based on Semantic Descriptions of Services
• Semantic Description of a Service– Describing the Capabilities of the Service
– Using ontology languages, such as OWL
– Referring to common domain ontologies
Slide 4
OverviewOverview
• Introduction
• Intuition behind modelling service semantics
• Operationalising discovery using logic
• Matching service descriptions
• Conclusion
Slide 5
Service Description – Service InstanceService Description – Service Instance
shipping1
BremenPlymouth
from to
packageXitem
50 kg
weight
set of accepted Service Instances
. . .
shipping2
HamburgDover
from to
barrelYitem
25 kg
weight
Service Instances
Service Description
Shipping containers from UK to Germany describes
Slide 6
Variance in Service DescriptionsVariance in Service Descriptions
• Two kinds of variance in service descriptions
toshipping2 Hamburg
. . .
toshipping1 Bremen
toshipping3 Boston
– due to incomplete knowledge
. . .
toshipping2 Hamburg
toshipping1 Bremen
Shipping to Germany
– due to intended diversity
differentservice
instances . . . differentpossible worlds
Slide 7
Discovery by Matching Service DescriptionsDiscovery by Matching Service Descriptions
• Matching Service Descriptions of Requestors an Providers
• If there are common instances, requestor and provider can (potentially) do business with each other
(Sr)I ∩ (Spi)I ≠ Ø
Sr
ServiceRequestor
Spi
ServiceProviders
Sp1
Spn
...
– How do their Service Descriptions intersect ?
Slide 8
OverviewOverview
• Introduction
• Intuition behind modelling service semantics
• Operationalising discovery using logic
• Matching service descriptions
• Conclusion
Slide 9
Intuition Intuition ↦↦ DL DL
• Service Description ↦ set of DL axioms D={1, ... , n}
– A service concept S occurring in some i
• Domain Knowledge ↦ DL knowledge base KB
Slide 10
Intuition Intuition ↦↦ DL DL
• Possible World ↦ Model I of KB ∪ D
• Service Instance ↦ relational structure in I
• acceptable Service Instances ↦ Extension SI of S
• Variance due to intended diversity ↦ |SI| ≥ 1
• Variance due to incompl. knowl. ↦ several Models I1, I2, ...
• Matching ↦ boolean function match(KB, Dr, Dp)– way of applying DL inferences
(Sr)I1
(UKCity)I1
(City)I1
item
fromfrom
(Package)I1 (Sr)I2
(UKCity)I2
(City)I2
item
from
(Package)I2
. . .
Slide 11
Towards Intuitive Modelling PrimitivesTowards Intuitive Modelling Primitives
Characterising Property Restrictions
• Multiplicity– single-valued
– multi-valued
• Variety– fixed value
– value range
• Availability– Mandatory
– obligatory
• Range Coverage– Covering
– non-covering
Slide 12
OverviewOverview
• Introduction
• Intuition behind modelling service semantics
• Operationalising discovery using logic
• Matching service descriptions
• Conclusion
Slide 13
Treating Variance in MatchingTreating Variance in Matching
• Resolving Incomplete Knowledge holds in every possible world :
Entailment KB ∪ Dr ∪ Dp ⊨ holds in some possible world :
Satisfiability KB ∪ Dr ∪ Dp ∪ {} sat.
• Resolving Intended Diversity– Request and Capability overlap :
Non-Disjointness = Sr ⊓ Sp ⋢ ⊥
– Request more specific than Capability :Subsumption = Sr ⊑ Sp
– Capability more specific than Request :Subsumption = Sp ⊑ Sr
⊨ sat.
⊑ ⊓
Slide 14
DL Inference for MatchingDL Inference for Matching
• Satisfiability of Concept Conjunction
(Sr ⊓ Sp) is satisfiable w.r.t. KB ∪ Dr ∪ Dp
(Sr)I1
(Sp)I1
. . .(Sr)I2
(Sp)I2
⊨ sat.
⊑ ⊓X
• (Sr)I ∩ (Sp)I ≠ Ø in some possible world
• Intuitiuon:– incomplete knowledge issues can be resolved such that request and
capability overlap
Slide 15
Satisfiability of Concept ConjunctionSatisfiability of Concept Conjunction
• Example:
⊨ sat.
⊑ ⊓X
• match(KB, Dr, DpA) = true
• match(KB, Dr, DpB) = true
– UKCity ⊓ USCity ⊑ ⊥ is not specified in KB
(Sr)I(UKCity)I
(City)I
Plymouth
Dublin from
from
(SpA)I
(USCity)I
(SpB)I
(Sr ⊓ Sp) is satisfiable
w.r.t. KB ∪ Dr ∪ Dp
Slide 16
DL Inference for MatchingDL Inference for Matching
• Entailment of concept subsumption
KB ∪ Dr ∪ Dp ⊨ Sr ⊑ Sp
• (Sr)I (Sp)I in every possible world
• Intuition:– the request is more specific than the capability regardless of how
incomplete knowledge issues are resolved
⊨ sat.
⊑ ⊓X
(Sr)I1
(Sp)I1
. . .
(Sr)I2
(Sp)I2
Slide 17
Entailment of Concept SubsumptionEntailment of Concept Subsumption
• Example:
• match(KB, Dr, DpA) = false
– Dublin outside the UK
⊨ sat.
⊑ ⊓X
(Sr)I
(UKCity)I
(City)I
Plymouth
Dublin from
from
(SpA)I
KB ∪ Dr ∪ Dp
⊨ Sr ⊑ Sp
Slide 18
DL Inference for MatchingDL Inference for Matching
• Entailment of Concept Non-Disjointness
KB ∪ Dr ∪ Dp ⊨ Sr ⊓ Sp ⋢ ⊥
• (Sr)I ∩ (Sp)I ≠ Ø in every possible world
• Intuition:– the request and the capability overlap regardless of how incomplete
knowledge issues are resolved
(Sr)I1
(Sp)I1
. . .
(Sr)I2
(Sp)I2
⊨ sat.
⊑ ⊓X
Slide 19
Entailment of Concept Non-DisjointnessEntailment of Concept Non-Disjointness
• Example:
• match(KB, Dr, DpA) = true
• match(KB, Dr, DpA) = false
– Plymouth outside the US in at least one possible world
⊨ sat.
⊑ ⊓X
(Sr)I(UKCity)I
(City)I
Plymouth
Dublin from
from
(SpA)I
(USCity)I
(SpB)I
KB ∪ Dr ∪ Dp
⊨ Sr ⊓ Sp ⋢ ⊥
Slide 20
Practicability of InferencesPracticability of Inferences
• Satisfiability of Concept Conjunction– very weak : vulnerable to false positive matches
– relies on additional disjointness constraints in domain ontologies
• Entailment of Concept Subsumption– Very strong : misses intuitively correct matches
• Entailment of Concept Non-Disjointness– Tries to overcome deficiencies of the other two inferences
– relies on range-covering property restrictions(problematic to express in DL)
Slide 21
Ranking Service DescriptionsRanking Service Descriptions
• Ranking based on Partial Subsumption
(SpA ⊓ Sr)I
• DL Inference
KB ∪ Dr ∪ DpA ∪ DpB ⊨ (SpA
⊓ Sr) ⊑ (SpB ⊓ Sr)
⇒ DpA ≼ DpB
(SpB ⊓ Sr)I
(SpA)I (SpB
)I
Slide 22
ConclusionConclusion
• Provided an intuitive semantics for formalService Descriptions based on Service Instances
• Emphasized the meaning of variance inService Descriptions
• Mapped intuitive notions to formal elements in DL
• Investigated different DL inferences for matching Service Descriptions
• Showed how variance can be treated during matching
• Proposed a ranking mechanism based on partial subsumption of Service Descriptions
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