Towards Fine-grained Service Matchmaking by Using Concept Similarity

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

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

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

6. 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.

7. Concept Similarity • Semantic distance: taking depth into account • Wu & Palmer • Li et al.

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. 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

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

14. 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

15. 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

16. 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  

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

18. NoM NoSM sim exact 1 1 level1 level2 0 . . . leveln fail 0 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

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

20. 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, …

21. Example

22. 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

23. 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?

24. Thanks!! Questions?