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1 Towards Fine-grained Service Matchmaking by Using Concept Similarity Alberto Fernández, Axel Polleres, Sascha Ossowski

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Presentation on theme: "1 Towards Fine-grained Service Matchmaking by Using Concept Similarity Alberto Fernández, Axel Polleres, Sascha Ossowski"— Presentation transcript:

1 1 Towards Fine-grained Service Matchmaking by Using Concept Similarity Alberto Fernández, Axel Polleres, Sascha Ossowski {alberto.fernandez,sascha.ossowski}@urjc.es axel.polleres@deri.org University Rey Juan Carlos (Madrid - Spain) DERI, National University of Ireland, Galway SMR2’07. ISWC, Busan. Nov. 11 – 15, 2007.

2 SMR2’07. ISWC, Busan. Nov. 11 – 15, 2007.2 Outline Introduction Concept Similarity Matching Semantic Web Services Towards a combined notion of similarity-based SM Conclusions

3 SMR2’07. ISWC, Busan. Nov. 11 – 15, 2007.3 Outline Introduction Concept Similarity Matching Semantic Web Services Towards a combined notion of similarity-based SM Conclusions

4 SMR2’07. ISWC, Busan. Nov. 11 – 15, 2007.4 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

5 SMR2’07. ISWC, Busan. Nov. 11 – 15, 2007.5 Outline Introduction Concept Similarity Matching Semantic Web Services Towards a combined notion of similarity-based SM Conclusions

6 SMR2’07. ISWC, Busan. Nov. 11 – 15, 2007.6 Concept Similarity Semantic distance approaches Rada et al.: Shortest path between two concepts in the taxonomy dist(c 1, c 2 ) = depth(c 1 ) + depth(c 2 ) − 2 × depth(lcs(c 1, c 2 )) Leacock & Chodorow Fernandez et al.

7 SMR2’07. ISWC, Busan. Nov. 11 – 15, 2007.7 Semantic distance: taking depth into account Wu & Palmer Li et al. Concept Similarity

8 SMR2’07. ISWC, Busan. Nov. 11 – 15, 2007.8 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

9 SMR2’07. ISWC, Busan. Nov. 11 – 15, 2007.9 Concept Similarity Information Content approaches pr(c) = probability of an individual being described by a specific concept c Resnik sim(c 1, c 2 ) = IC(lcs(c 1, c 2 )) = −log pr(lcs(c 1, c 2 )) Jiang & Conrath sim(c 1, c 2 ) = IC(c 1 ) + IC(c 2 ) − 2 × IC(lcs(c 1, c 2 )) Lin

10 SMR2’07. ISWC, Busan. Nov. 11 – 15, 2007.10 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 & d’Amato 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

11 SMR2’07. ISWC, Busan. Nov. 11 – 15, 2007.11 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).

12 SMR2’07. ISWC, Busan. Nov. 11 – 15, 2007.12 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

13 SMR2’07. ISWC, Busan. Nov. 11 – 15, 2007.13 Outline Introduction Concept Similarity Matching Semantic Web Services Towards a combined notion of similarity-based SM Conclusions

14 SMR2’07. ISWC, Busan. Nov. 11 – 15, 2007.14 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 …

15 SMR2’07. ISWC, Busan. Nov. 11 – 15, 2007.15 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: OUT R and OUT S are equivalent or OUT R subclass of OUT S Plug In: OUT S subsumes OUT R Subsumes: OUT R subsumes OUT S 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

16 SMR2’07. ISWC, Busan. Nov. 11 – 15, 2007.16 Matching SWS: notions of match OWLS-MX Hybrid: Logic based + Syntactic IR based similarity Matching filters Exact:  IN S  IN R : IN S = IN R   OUT R  OUT S : OUT R = OUT S Plug In:  IN S  IN R : IN S  IN R   OUT R  OUT S : OUT S  LSC(OUT R ) Subsumes:  IN S  IN R : IN S  IN R   OUT R  OUT S : OUT R  OUT S Subsumed-by:  IN S  IN R : IN S  IN R   OUT R  OUT S : ( OUT S = OUT R  OUT S  LGC(OUT R ))  SIM IR (S,R)   Logic-based fail: above logic based filters fail Nearest-neighbour:  IN S  IN R : IN S  IN R   OUT R  OUT S : OUT R  OUT S  SIM IR (S,R)   Fail

17 SMR2’07. ISWC, Busan. Nov. 11 – 15, 2007.17 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  

18 SMR2’07. ISWC, Busan. Nov. 11 – 15, 2007.18 Outline Introduction Concept Similarity Matching Semantic Web Services Towards a combined notion of similarity-based SM Conclusions

19 SMR2’07. ISWC, Busan. Nov. 11 – 15, 2007.19 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, level 1, level 2, …, level n, 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 0 1 sim 1 0 NoSMNoM level 1 level 2 level n exact fail......

20 SMR2’07. ISWC, Busan. Nov. 11 – 15, 2007.20 Outline Introduction Concept Similarity Matching Semantic Web Services Towards a combined notion of similarity-based SM Conclusions

21 SMR2’07. ISWC, Busan. Nov. 11 – 15, 2007.21 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, …

22 SMR2’07. ISWC, Busan. Nov. 11 – 15, 2007.22 Example

23 SMR2’07. ISWC, Busan. Nov. 11 – 15, 2007.23 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

24 SMR2’07. ISWC, Busan. Nov. 11 – 15, 2007.24 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?

25 25 Thanks!! Questions?

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