FZI
Research Center for Information Science
at the University of Karlsruhe
Variance in e-Business
Service Discovery
Stephan Grimm,
Boris Motik,
Chris Preist (HP Labs, Bristol)
Forschungszentrum Informatik, Karlsruhe
Overview
• Introduction
• Intuition behind modelling service semantics
• Operationalising discovery using logic
• Matching service descriptions
• Conclusion
Slide 2
Service 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 3
Overview
• Introduction
• Intuition behind modelling service semantics
• Operationalising discovery using logic
• Matching service descriptions
• Conclusion
Slide 4
Service Description – Service Instance
set of accepted
Service Instances
Shipping
containers
from UK
to Germany
describes
Service Description
item
shipping1
from
Plymouth
to
Bremen
packageX
weight
50 kg
item
shipping2
from
Dover
to
Hamburg
barrelY
weight
...
25 kg
Service Instances
Slide 5
Variance in Service Descriptions
• Two kinds of variance in service descriptions
– due to intended diversity
– due to incomplete knowledge
Shipping
to Germany
different
service
instances
. . .
to
Bremen
shipping1
to
Bremen
shipping2
to
Hamburg
shipping2
to
Hamburg
shipping3
to
Boston
...
shipping1
different
possible
worlds
...
Slide 6
Discovery by Matching Service Descriptions
• Matching Service Descriptions of Requestors an Providers
– How do their Service Descriptions intersect ?
(Sr)I ∩ (Spi)I ≠ Ø
Sr
Spi
Service
Requestor
Sp1
...
Service
Providers
Spn
• If there are common instances, requestor and provider can
(potentially) do business with each other
Slide 7
Overview
• Introduction
• Intuition behind modelling service semantics
• Operationalising discovery using logic
• Matching service descriptions
• Conclusion
Slide 8
Intuition ↦ 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 9
Intuition ↦ 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
(Package)I1
(Sr)I1
from
item
(Package)I2
(Sr)I2
item
from
from
(City)I1
(City)I2
(UKCity)I1
...
(UKCity)I2
Slide 10
Towards Intuitive Modelling Primitives
Characterising Property Restrictions
• Variety
– fixed value
– value range
• Availability
– Mandatory
– obligatory
• Multiplicity
– single-valued
– multi-valued
• Range Coverage
– Covering
– non-covering
Slide 11
Overview
• Introduction
• Intuition behind modelling service semantics
• Operationalising discovery using logic
• Matching service descriptions
• Conclusion
Slide 12
Treating Variance in Matching
• Resolving Incomplete Knowledge
–  holds in every possible world :
Entailment
KB ∪ Dr ∪ Dp ⊨ 
⊨
 sat.
⊑⊓
–  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
Slide 13
DL Inference for Matching
• Satisfiability of Concept Conjunction
(Sp)I1
(Sp)I2
(Sr)I1
(Sr)I2
X
(Sr ⊓ Sp) is satisfiable w.r.t. KB ∪ Dr ∪ Dp
⊨
 sat.
⊑⊓
. . .
• (Sr)I ∩ (Sp)I ≠ Ø in some possible world
• Intuitiuon:
– incomplete knowledge issues can be resolved such that request and
capability overlap
Slide 14
Satisfiability of Concept Conjunction
• Example:
X
⊨
 sat.
⊑⊓
(Sr ⊓ Sp) is satisfiable
w.r.t. KB ∪ Dr ∪ Dp
(UKCity)I
(S )I
r
(USCity)I
• match(KB, Dr, DpA) = true
• match(KB, Dr, DpB) = true
Plymouth
(City)I
Dublin
(SpA)I
from
from
(SpB)I
– UKCity ⊓ USCity ⊑ ⊥ is not specified in KB
Slide 15
DL Inference for Matching
• Entailment of concept subsumption
KB ∪ Dr ∪ Dp ⊨ Sr ⊑ Sp
(Sp)I1
(Sr)I1
(Sr)I2
X
⊨
 sat.
⊑⊓
(Sp)I2
. . .
• (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
Slide 16
Entailment of Concept Subsumption
• Example:
X
⊨
 sat.
⊑⊓
KB ∪ Dr ∪ Dp
⊨ Sr ⊑ Sp
(UKCity)I
(SpA)I
Plymouth
• match(KB, Dr, DpA) = false
(City)I
Dublin
from
from
(Sr)I
– Dublin outside the UK
Slide 17
DL Inference for Matching
• Entailment of Concept Non-Disjointness
KB ∪ Dr ∪ Dp ⊨ Sr ⊓ Sp ⋢ ⊥
(Sp)I1
(Sr)I2
X
⊨
 sat.
⊑⊓
(Sp)I2
(Sr)I1
. . .
• (Sr)I ∩ (Sp)I ≠ Ø in every possible world
• Intuition:
– the request and the capability overlap regardless of how incomplete
knowledge issues are resolved
Slide 18
Entailment of Concept Non-Disjointness
• Example:
X
⊨
 sat.
⊑⊓
KB ∪ Dr ∪ Dp
⊨ Sr ⊓ Sp ⋢ ⊥
(UKCity)I
(USCity)I
I
• match(KB, Dr, DpA) = true (City)
• match(KB, Dr, DpA) = false
(Sr)I
Plymouth
Dublin
(SpA)I
from
from
– Plymouth outside the US in at least one possible world
(SpB)I
Slide 19
Practicability 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 20
Ranking Service Descriptions
• Ranking based on Partial Subsumption
(SpA)I
(SpB ⊓ Sr)I
(SpB)I
⇒
DpA ≼ DpB
(SpA ⊓ Sr)I
• DL Inference
KB ∪ Dr ∪ DpA ∪ DpB ⊨ (SpA ⊓ Sr) ⊑ (SpB ⊓ Sr)
Slide 21
Conclusion
• Provided an intuitive semantics for formal
•
•
•
•
•
Service Descriptions based on Service Instances
Emphasized the meaning of variance in
Service 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
Slide 22