Metrics-based Semantic Web Service Discovery, IEEE ICWS-2009
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Metrics-based Semantic Web Service Discovery, IEEE ICWS-2009

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Slideset presented at research track at IEEE 7th International Conference on Web Services (ICWS 2009), July 6-10, 2009, Los Angeles, CA, USA.

Slideset presented at research track at IEEE 7th International Conference on Web Services (ICWS 2009), July 6-10, 2009, Los Angeles, CA, USA.

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Metrics-based Semantic Web Service Discovery, IEEE ICWS-2009 Presentation Transcript

  • 1. Exploiting Metrics for Semantic Web Service Discovery - IEEE ICWS’09, Los Angeles, CA, USA, July 08, 2009 - Stefan Dietze, Alessio Gugliotta, John Domingue Knowledge Media Institute, The Open University, UK
  • 2. Outline Semantic Web Services (SWS) mediation Two-fold representation approach for SWS Prototypical application Conclusions
  • 3. Introduction Semantic Web Services Formal specifications of Web services in terms of their capabilities (Cap), interfaces (If) and non-functional properties (Nfp) Capabilities describe assumptions (Ass) and effects (Eff) sws:WebService sws:WebService sws:WebService SWS.1 SWS.2 SWS.3 Defined through ontologies O, i.e. as tuple of concepts C, instances I, properties P, relations R and axioms A WebService WebService WebService Reference models such as OWL-S, WS.1 WS.2 WS.3 WSMO, SAWSDL
  • 4. Introduction Semantic Mediation for SWS Discovery sws:Request SWS discovery: matchmaking of R.1 capabilities of SWS e.g. : As 2 ⊂ As1 ∪ Ef 2 ⊂ Ef1 ? ? SWS brokers match logical expressions sws:WebService sws:WebService sws:WebService (e.g. As1 ≡ ¬ I1 ∩ I 2 & As 2 ≡ I 3 ∩ ¬ I 4 ) SWS.1 SWS.2 SWS.3 WebService WebService WebService WS.1 WS.2 WS.3
  • 5. Introduction Semantic Mediation for SWS Discovery sws:Request SWS discovery: matchmaking of R.1 capabilities of SWS e.g. : As 2 ⊂ As1 ∪ Ef 2 ⊂ Ef1 Semantic-Level Mediation SWS brokers match logical expressions sws:WebService sws:WebService sws:WebService (e.g. As1 ≡ ¬ I1 ∩ I 2 & As 2 ≡ I 3 ∩ ¬ I 4 ) SWS.1 SWS.2 SWS.3 Heterogeneous SWS annotations Semantic mediation: Mediation between different semantic alignment/mapping of SWS representations concepts/instances across distinct WebService WebService WebService SWS WS.1 WS.2 WS.3
  • 6. Semantic Mediation Issues SWS Request (Consumer) SWS1 (get-flowers) has-assumption As1 ≡ ¬I1 ∩ I 2 <Color rdf:ID="Lilac"/> <Location rdf:ID="Milton_Keynes"/>
  • 7. Semantic Mediation Issues SWS Request (Consumer) SWS (Provider) SWS1 SWS2 (get-flowers) (provide-flowers) has-assumption has-assumption As1 ≡ ¬I1 ∩ I 2 As 2 ≡ I 3 ∩ ¬ I 4 <Color rdf:ID="Lilac"/> <Colour rdf:ID="Purple"/> <Location rdf:ID="Milton_Keynes"/> <geospatialLocation rdf:ID="M-K"/>
  • 8. Semantic Mediation Issues SWS Request (Consumer) SWS (Provider) SWS1 SWS2 (get-flowers) (provide-flowers) has-assumption has-assumption As1 ≡ ¬I1 ∩ I 2 As 2 ≡ I 3 ∩ ¬ I 4 <Color rdf:ID="Lilac"/> ? <Colour rdf:ID="Purple"/> <Location rdf:ID="Milton_Keynes"/> <geospatialLocation rdf:ID="M-K"/>
  • 9. Semantic Mediation Issues Issues: Heterogeneous SWS annotations Lack of implicit similarity information (symbol grounding problem) Reliance on either: (i) manual mapping rules (ii) ontology mapping mechanisms (exploiting linguistic or structural similarities) Costly and error-prone => representations which implicitly represent similarities across heterogeneous SWS ?
  • 10. Two-fold Approach Refining SWS through Conceptual Spaces Refining SWS ontologies through multiple Conceptual Spaces (CS), i.e. multidimensional, geometrical vector spaces Concept C in one SWS ontology O => Conceptual Space CS Instance I of C => member M (vector) in CS SWS Ontology O1 Concept C1x instance-of instance-of refined-as-cs Instance I1i Instance I1j d1 refined-as-member refined-as-member d2 d3 Conceptual Space CS1x
  • 11. Two-fold Approach Refining SWS through Conceptual Spaces Similarity-computation between SWS instances by means of spatial distances in CS Common agreement at schema (i.e. CS) level Facilitated through wide-spread use of upper-level ontologies (DOLCE, SUMO…) Agent 1 Agent 2 SWS Ontology O1 SWS Ontology O2 Concept c1x Concept c2x instance-of instance-of refined-as-cs refined-as-cs Instance i1i Instance i2i refined-as-member d1 refined-as-member d2 d3 Conceptual Space CSx
  • 12. Two-fold Approach Formal CS Ontology CS ontology enabling to represent SWS concepts / instances through CS: CS (refining concept C) represented through set of dimensions di each associated with a prominence value pi: CS n = {( p1d1 , p2 d 2 ,..., pn d n ) d i ∈ CS , pi ∈ P} , Assignment of measurement scales Dimension dj might be refined by further dimensions: d = Dn = {( p' d ' , p' d ' ,..., p' d ' ) d ' ∈ D} (CS possibly composed of subspaces), j 1 1 2 2 n n k
  • 13. Two-fold Approach Formal CS Ontology CS ontology enabling to represent SWS concepts / instances through CS: CS (refining concept C) represented through set of dimensions di each associated with a prominence value pi: CS n = {( p1d1 , p2 d 2 ,..., pn d n ) d i ∈ CS , pi ∈ P} , Assignment of measurement scales Dimension dj might be refined by further dimensions: d = Dn = {( p' d ' , p' d ' ,..., p' d ' ) d ' ∈ D} (CS possibly composed of subspaces), j 1 1 2 2 n n k Members M (refining instances I) represented through vectors in CS: M n = {(v1 , v2 ,..., vn ) vi ∈ M } Similarity between two SWS instances (members) V and U calculated by means of their Euclidean distance: n u −u v −v 2 dist (u, v) = ∑ pi (( i )−( i )) i =1 su sv (where u is the mean of a dataset U and su is the standard deviation from U)
  • 14. A Similarity-based Mediator Implementation based on WSMO Implementation based on Web Service Modelling Ontology (WSMO) and SWS reasoning environment IRS-III for similarity-based SWS discovery wsmo:Goal G.1 (1) (2) wsmo:Mediator wsmo:MedWS Med.1 SWS.1.1 Comp. Sim. (3) (4) wsmo:WebService wsmo:WebService wsmo:WebService SWS.1 SWS.2 SWS.3 (5)
  • 15. A Similarity-based Mediator Implementation based on WSMO Implementation based on Web Service Modelling Ontology (WSMO) and SWS reasoning environment IRS-III for similarity-based SWS discovery WSMO Mediator: computation of similarities between given request (WSMO Goal, G1) and set of x associated SWS (SWS1..SWSx) Implemented through mediation Web service wsmo:Goal G.1 (1) (2) wsmo:Mediator wsmo:MedWS Med.1 SWS.1.1 Comp. Sim. (3) (4) wsmo:WebService wsmo:WebService wsmo:WebService SWS.1 SWS.2 SWS.3 (5)
  • 16. Measurement-based Service Selection Implementation based on WSMO MedWS SWS1.1 computes x similarity values with Sim(G1,SWSj) defined as reciprocal to the −1 mean value of their individual member distances:  n   ∑ (dist k )  ( ) Sim(Gi , SWS j ) = Dist (Gi , SWS j ) =  k =1 −1  n       With distk being the distance between one particular member vi of G1 and one member of SWSj in the same CS wsmo:Goal G.1 (1) (2) wsmo:Mediator wsmo:MedWS Med.1 SWS.1.1 Comp. Sim. (3) (4) wsmo:WebService wsmo:WebService wsmo:WebService SWS.1 SWS.2 SWS.3 (5)
  • 17. SWS Mediation based on CS Prototypical Application Uses representational approach based on CS and similarity-based WSMO Mediator Aims at retrieval of distributed video resources (provided within EU FP7 IP NoTube - http://notube.tv) Keyword-based searches across 5 (REST) Web services built on top of the following (logical/physical) repositories BBC Backstage (world news feed) [ http://backstage.bbc.co.uk/ ] Open Video [ http://www.open-video.org/ ] YouTube (entertainment feed) [ http://www.youtube.com ] OU channel on YouTube [ http://www.youtube.com/ou ] YouTube (mobile feed) [ http://www.youtube.com/ou ] Similarity-based service discovery for a given request
  • 18. SWS Mediation based on CS Prototypical Application SWS6: get-video-request M6 ={v1, v2, v3} M6 ={v1, v2} 1 2 CS1 Purpose Space CS2 Environment Space O1:Purp O1:Env O2:Purp O2:Env O3:Purp O3:Env O4:Purp O4:Env O5:Purp O5:Env SWS1: SWS2: SWS3: SWS4: SWS5: OU-youtube entertain-youtube open-video bbc-backstage mobile-youtube WS1: WS2: WS3: WS4: WS5: OU-youtube entertain-youtube open-video bbc-backstage mobile-youtube
  • 19. SWS Mediation based on CS Prototypical Application SWS6: get-video-request M6 ={v1, v2, v3} M6 ={v1, v2} 1 2 CS1 Purpose Space CS2 Environment Space O1:Purp O1:Env O2:Purp O2:Env O3:Purp O3:Env O4:Purp O4:Env O5:Purp O5:Env SWS1: SWS2: SWS3: SWS4: SWS5: OU-youtube entertain-youtube open-video bbc-backstage mobile-youtube WS1: WS2: WS3: WS4: WS5: OU-youtube entertain-youtube open-video bbc-backstage mobile-youtube
  • 20. SWS Mediation based on CS Prototypical Application SWS6: get-video-request M6 ={v1, v2, v3} M6 ={v1, v2} 1 2 {(p1*information, p2*education, p3*leisure)} = CS1 {(p4*resolution, p5*bandwidth)} = CS2 CS1 Purpose Space CS2 Environment Space O1:Purp O1:Env O2:Purp O2:Env O3:Purp O3:Env O4:Purp O4:Env O5:Purp O5:Env SWS1: SWS2: SWS3: SWS4: SWS5: OU-youtube entertain-youtube open-video bbc-backstage mobile-youtube WS1: WS2: WS3: WS4: WS5: OU-youtube entertain-youtube open-video bbc-backstage mobile-youtube
  • 21. SWS Mediation based on CS Prototypical Application SWS annotated with Assumption Ass defined through conjunction of instances (more expressive logical expressions to be considered) Instances refined through vectors (members) Assumption Ass SWSi = ( P1SWSi ∪ P2 SWSi ∪ .. ∪ PnSWSi ) ∪ ( E 1SWSi ∪ E 2 SWSi ∪ .. ∪ E mSWSi ) Members Pi in CS1 (purpose) Members Ej in CS2 (environment) SWS1 P1(SWS1)={(0, 100, 0)} E1(SWS1)={(100, 100)} SWS2 P1(SWS2)={(0, 0, 100)} E1(SWS2)={(100, 100)} SWS3 P1(SWS3)={(50, 50, 0)} E1(SWS3)={(100, 100)} SWS4 P1(SWS4)={(100, 0, 0)} E1(SWS4)={(100, 100)} P1(SWS5)={(100, 0, 0)} SWS5 E1(SWS5)={(10, 10)} P2(SWS5)={(0, 100, 0)}
  • 22. SWS Mediation based on CS Prototypical Application SWS annotated with Assumption Ass defined through conjunction of instances (more expressive logical expressions to be considered) Instances refined through vectors (members) OU@Youtube SWS Educational purpose High resolution and bandwidth Assumption Ass SWSi = ( P1SWSi ∪ P2 SWSi ∪ .. ∪ PnSWSi ) ∪ ( E 1SWSi ∪ E 2 SWSi ∪ .. ∪ E mSWSi ) Members Pi in CS1 (purpose) Members Ej in CS2 (environment) SWS1 P1(SWS1)={(0, 100, 0)} E1(SWS1)={(100, 100)} SWS2 P1(SWS2)={(0, 0, 100)} E1(SWS2)={(100, 100)} SWS3 P1(SWS3)={(50, 50, 0)} E1(SWS3)={(100, 100)} SWS4 P1(SWS4)={(100, 0, 0)} E1(SWS4)={(100, 100)} P1(SWS5)={(100, 0, 0)} SWS5 E1(SWS5)={(10, 10)} P2(SWS5)={(0, 100, 0)}
  • 23. SWS Mediation based on CS Prototypical Application SWS6: get-video-request M6 ={v1, v2, v3} M6 ={v1, v2} 1 2 CS1 Purpose Space CS2 Environment Space Requests (WSMO Goals) defined through AJAX-based UI O1:Purp O1:Env O2:Purp O2:Env O3:Purp O3:Env O4:Purp O4:Env O5:Purp O5:Env Requests: consists of: SWS SWS2: SWS3: SWS4: SWS5: 1 OU-youtube entertain-youtube open-video bbc-backstage mobile-youtube Input parameters: set of keywords (free text) Assumption: defined through vectors WS : WS : WS : WS : WS5: (measurements describing purpose and environment) 4 1 OU-youtube 2 entertain-youtube 3 open-video bbc-backstage mobile-youtube Similarity-based discovery of most suitable service based on WSMO mediator
  • 24. DEMO SWS6: get-video-request M6 ={v1, v2, v3} M6 ={v1, v2} 1 2 CS1 Purpose Space CS2 Environment Space O1:Purp O1:Env O2:Purp O2:Env O3:Purp O3:Env O4:Purp O4:Env O5:Purp O5:Env SWS1: SWS2: SWS3: SWS4: SWS5: OU-youtube entertain-youtube open-video bbc-backstage mobile-youtube WS1: WS2: WS3: WS4: WS5: OU-youtube entertain-youtube open-video bbc-backstage mobile-youtube
  • 25. Conclusions Discussion and Summary Some issues: SWS assumptions defined as conjunction of instances => consideration of arbitrary logical expressions required Additional representational effort Similarity-calculation requires overlapping CS and measurable quality dimensions
  • 26. Conclusions Discussion and Summary Some issues: SWS assumptions defined as conjunction of instances => consideration of arbitrary logical expressions required Additional representational effort Similarity-calculation requires overlapping CS and measurable quality dimensions …, however: Allows to compute similarities between distinct SWS Reduces the required level of common ontological agreement (only required at schema level).
  • 27. Thank you! E-mail: s.dietze@open.ac.uk Web: http://people.kmi.open.ac.uk/dietze