Towards Finegrained Service Matchmaking by Using Concept Similarity

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Towards Fine-grained Service Matchmaking by
Using Concept Similarity

• Alberto Fernández, Axel Polleres, Sascha Ossowski
  
• alberto.fernandez,sascha.ossowski_at_urjc.es
• axel.polleres_at_deri.org
• University Rey Juan Carlos (Madrid - Spain)
• DERI, National University of Ireland, Galway

SMR207. ISWC, Busan. Nov. 11 15, 2007.
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Outline

• Introduction
• Concept Similarity
• Matching Semantic Web Services
• Towards a combined notion of similarity-based SM
• Conclusions

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Outline

• Introduction
• Concept Similarity
• Matching Semantic Web Services
• Towards a combined notion of similarity-based SM
• Conclusions

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Introduction

• Location and selection of services in SOA
• Service Descriptions
• Provided services (advertisements)
• Service requests
• Both based on shared formal ontologies
• Notions of match between advertisements and
  requests
• Subsumption checking
• Boolean (or several degrees of) match
• Concept similarity
• Numerical (fine grained)
• Objective
• Unified framework Notions of match concept
  similarity

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Outline

• Introduction
• Concept Similarity
• Matching Semantic Web Services
• Towards a combined notion of similarity-based SM
• Conclusions

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Concept Similarity

• Semantic distance approaches
• Rada et al. Shortest path between two concepts
  in the taxonomy
• dist(c1, c2) depth(c1) depth(c2) - 2
  depth(lcs(c1, c2))
• Leacock Chodorow
• Fernandez et al.

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Concept Similarity

• Semantic distance taking depth into account
• Wu Palmer
• Li et al.

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Concept Similarity

• Feature-based approaches (Tversky)
• Contrast model
• contrast(C,D) f(ftrs(C)? ftrs(D))-f(ftrs(C)\ftrs
  (D))-f(ftrs(D)\ftrs(C))
• f() is usually the count of features, ftrs(C)
  set of features in C
• number of common minus the number of non-common
  features
• Ratio model
• Which is commonly taken as

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Concept Similarity

• Information Content approaches
• pr(c) probability of an individual being
  described by a specific concept c
• Resnik
• sim(c1, c2) IC(lcs(c1, c2)) -log pr(lcs(c1,
  c2))
• Jiang Conrath
• sim(c1, c2) IC(c1) IC(c2) - 2 IC(lcs(c1,
  c2))
• Lin

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Concept Similarity

• Description Logics approaches
• Borgida et al.
• Applyies distance, feature and information
  content models
• Very simple DL (A) only conjunctions
• Di Noia et al.
• potential match (some requests in demand D are
  not specified in S)
• the number of concepts names in D not in S,
• the number of number restrictions of D not
  implied by those of S
• add recursively rankPotential for each universal
  role quantification in D
• Fanizzi dAmato
• define a similarity measure between concepts in
  ALN DL.
• decompose the normal form of the concept
  descriptions
• Primitive concepts ratio of common individuals
  wrt. either conjunct.
• Value restrictions computed recursively, the
  average value is taken.
• Numeric restrictions ratio of overlap, the
  average value is taken

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Concept Similarity

• Information Retrieval approaches
• OWLS-MX (Klusch et al.)
• logic-based reasoning is complemented by IR based
  similarity
• four different token-based string metrics
• the cosine
• the loss of information
• the extended Jacquard
• Jensen-Shannon information divergence
• applied to unfolded concepts
• (and C (and B (and A))) corresponds to the
  concept C (C ? B ? A).

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Concept Similarity compound concepts

• Rada et al.
• Disjunction
• Conjunction
• Ehrig et al. (cosine)
• (sim(e, e1), sim(e, e2), . . . ,
• sim(e, f1), sim(e, f2), . . .),
• Sierra Debenham

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Outline

• Introduction
• Concept Similarity
• Matching Semantic Web Services
• Towards a combined notion of similarity-based SM
• Conclusions

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Matching Semantic Web Services

• Components of Service Descriptions
• Service Taxonomies
• E.g. CarRentalService, CreditCardAccountService
• Operations
• E.g. RequestCreditCardBalance, BookRentalCar
• Inputs/Outputs
• Preconditions/Postconditions
• Logical formulae not expressed in an ontological
  hierarchy
• Not exploited by current approaches

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Matching SWS notions of match

• Paolucci et al.
• An advertisement (S) matches a request (R) iff
• for each output of R there is a matching output
  in S.
• for each input of S there is a matching input in
  R.
• Degree of match for outputs (inverse for inputs)
• Exact OUTR and OUTS are equivalent or OUTR
  subclass of OUTS
• Plug In OUTS subsumes OUTR
• Subsumes OUTR subsumes OUTS
• Fail no subsumption relation
• If there are several outputs with different
  degree of match, the minimum degree is used
• The set of service advertisements is sorted by
  comparing output matches first

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Matching SWS notions of match

• OWLS-MX
• Hybrid Logic based Syntactic IR based
  similarity
• Matching filters
• Exact ?INS ? INR INS INR ? ?OUTR ? OUTS OUTR
  OUTS
• Plug In
• ? INS ? INR INS ? INR ? ? OUTR ? OUTS OUTS
  ?LSC(OUTR)
• Subsumes
• ? INS ? INR INS ? INR ? ? OUTR ? OUTS OUTR ?
  OUTS
• Subsumed-by
• ? INS ? INR INS ? INR ? ? OUTR ? OUTS (OUTS
  OUTR ? OUTS?LGC(OUTR)) ? SIMIR(S,R) ? ?
• Logic-based fail above logic based filters fail
• Nearest-neighbour
• ?INS ?INR INS ? INR ? ? OUTR ? OUTS OUTR ? OUTS
  ? SIMIR(S,R) ? ?
• Fail

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Matching SWS notions of match

• Li Horrocks
• One DL concept defines the inputs and one the
  outputs
• Extend the degree levels proposed by Paolucci
• Exact if S R
• Plug In if R ? S
• Subsume if S ? R
• Intersection if ?(S?R ? ?)
• Disjoint if S ? R ? ?

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Outline

• Introduction
• Concept Similarity
• Matching Semantic Web Services
• Towards a combined notion of similarity-based SM
• Conclusions

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Towards a combined notion of simil.-based SM

• Notion of similarity match (NoSM)
• Real number in 0..1
• Notion of match
• Logic-based, coarse grained
• Several levels of match
• NoM ? exact, level1, level2, , leveln, fail
• Refining with concept similarity (sim)
• Real number in 0..1
• Aggregation
• Compound concepts (e.g. set of inputs)
• Components Inputs, Outputs, Operations
• Maintaining NoM (logic-based) semantic

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Outline

• Introduction
• Concept Similarity
• Matching Semantic Web Services
• Towards a combined notion of similarity-based SM
• Conclusions

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Conclusions

• Concept Similarity
• Distance is commonly used
• Assumes equally distributed instances over
  concepts
• Difficult to apply to DL
• Adoption of canonical representation? Spanning
  tree of pre-classification, new atomic concept
  names for ?R.C, ?R.C,

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Example
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Conclusions

• Concept Similarity
• Distance is commonly used
• Assumes equally distributed instances over
  concepts
• Difficult to apply to DL
• Adoption of canonical representation? Spanning
  tree of pre-classification, new atomic concept
  names for ?R.C, ?R.C,
• but other approaches exist (features, IC, IR )
• Concept definitions vs instances
• Matching SWS
• Most current approaches based on inputs/outputs
• Logic based reasoning subsumption
• Several (non-numerical) degrees of match

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Conclusions and further work

• Notion of similarity-based service matching
• Using concept similarity to refine notion of
  match
• Fine-grained degree of match facilitates service
  ranking
• Open issues
• Which service description framework to focus on?
  OWL-S, WSMO, etc, or a new one to which these
  approaches could be easily mapped?
• Which concept similarity measure better fits our
  framework? Is there a single best measure? What
  are the conditions that it must fulfill?
• How should values corresponding to different
  elements be combined?
• Do different applications require the same
  framework or should it be adapted for each of
  them?

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Thanks!!

• Questions?