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Exploiting Metrics for Semantic Web Service Discovery
 

Exploiting Metrics for Semantic Web Service Discovery

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    Exploiting Metrics for Semantic Web Service Discovery Exploiting Metrics for Semantic Web Service Discovery Presentation Transcript

    • 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
      • Semantic Web Services (SWS) mediation
      • Two-fold representation approach for SWS
      • Prototypical application
      • Conclusions
      Outline
    • 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)
        • Defined through ontologies O, i.e. as tuple of concepts C , instances I , properties P , relations R and axioms A
        • Reference models such as OWL-S, WSMO, SAWSDL
      sws:WebService SWS.2 sws:WebService SWS.3 sws:WebService SWS.1 WebService WS.2 WebService WS.3 WebService WS.1
      • SWS discovery: matchmaking of capabilities of SWS e.g. :
      • SWS brokers match logical expressions (e.g. & )
      Introduction Semantic Mediation for SWS Discovery sws:WebService SWS.2 sws:WebService SWS.3 sws:Request R.1 sws:WebService SWS.1 WebService WS.2 WebService WS.3 WebService WS.1 ? ?
      • SWS discovery: matchmaking of capabilities of SWS e.g. :
      • SWS brokers match logical expressions (e.g. & )
      • Heterogeneous SWS annotations
      • Semantic mediation: alignment/mapping of SWS concepts/instances across distinct SWS
      Introduction Semantic Mediation for SWS Discovery sws:WebService SWS.2 sws:WebService SWS.3 sws:Request R.1 sws:WebService SWS.1 WebService WS.2 WebService WS.3 WebService WS.1 Semantic-Level Mediation Mediation between different semantic representations
    • Semantic Mediation Issues SWS 1 (get-flowers) <Color rdf:ID=&quot;Lilac&quot;/> <Location rdf:ID=&quot;Milton_Keynes&quot;/> has-assumption SWS Request (Consumer)
    • Semantic Mediation Issues SWS 1 (get-flowers) <Color rdf:ID=&quot;Lilac&quot;/> <Location rdf:ID=&quot;Milton_Keynes&quot;/> SWS 2 (provide-flowers) <Colour rdf:ID=&quot;Purple&quot;/> <geospatialLocation rdf:ID=&quot;M-K&quot;/> has-assumption has-assumption SWS (Provider) SWS Request (Consumer)
    • Semantic Mediation Issues SWS 1 (get-flowers) <Color rdf:ID=&quot;Lilac&quot;/> <Location rdf:ID=&quot;Milton_Keynes&quot;/> SWS 2 (provide-flowers) <Colour rdf:ID=&quot;Purple&quot;/> <geospatialLocation rdf:ID=&quot;M-K&quot;/> has-assumption has-assumption ? SWS (Provider) SWS Request (Consumer)
      • 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 ?
      Semantic Mediation Issues
        • 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
      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…)
      Two-fold Approach Refining SWS through Conceptual Spaces
      • CS ontology enabling to represent SWS concepts / instances through CS:
        • CS (refining concept C ) represented through set of dimensions d i each associated with a prominence value p i : ,
        • Assignment of measurement scales
        • Dimension d j might be refined by further dimensions: (CS possibly composed of subspaces),
      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 d i each associated with a prominence value p i : ,
        • Assignment of measurement scales
        • Dimension d j might be refined by further dimensions: (CS possibly composed of subspaces),
        • Members M (refining instances I ) represented through vectors in CS:
        • Similarity between two SWS instances (members) V and U calculated by means of their Euclidean distance: ( where is the mean of a dataset U and s u is the standard deviation from U)
      Two-fold Approach Formal CS Ontology
    • 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
    • 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, G 1 ) and set of x associated SWS ( SWS 1 ..SWS x )
      • Implemented through mediation Web service
      • MedWS SWS 1.1 computes x similarity values with Sim(G 1 ,SWS j ) defined as reciprocal to the mean value of their individual member distances:
      • With dist k being the distance between one particular member v i of G 1 and one member of SWS j in the same CS
      Measurement-based Service Selection Implementation based on WSMO
      • 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
      SWS Mediation based on CS Prototypical Application
    • SWS Mediation based on CS Prototypical Application SWS 1 : OU-youtube O 1 :Purp O 1 :Env SWS 2 : entertain-youtube O 2 :Purp O 2 :Env SWS 3 : open-video O 3 :Purp O 3 :Env SWS 4 : bbc-backstage O 4 :Purp O 4 :Env M 6 2 ={v 1 , v 2 } SWS 5 : mobile-youtube O 5 :Purp O 5 :Env CS 2 Environment Space CS 1 Purpose Space SWS 6 : get-video-request M 6 1 ={v 1, v 2 , v 3 } WS 1 : OU-youtube WS 2 : entertain-youtube WS 3 : open-video WS 4 : bbc-backstage WS 5 : mobile-youtube
    • SWS Mediation based on CS Prototypical Application SWS 1 : OU-youtube O 1 :Purp O 1 :Env SWS 2 : entertain-youtube O 2 :Purp O 2 :Env SWS 3 : open-video O 3 :Purp O 3 :Env SWS 4 : bbc-backstage O 4 :Purp O 4 :Env M 6 2 ={v 1 , v 2 } SWS 5 : mobile-youtube O 5 :Purp O 5 :Env CS 2 Environment Space CS 1 Purpose Space SWS 6 : get-video-request M 6 1 ={v 1, v 2 , v 3 } WS 1 : OU-youtube WS 2 : entertain-youtube WS 3 : open-video WS 4 : bbc-backstage WS 5 : mobile-youtube
    • SWS Mediation based on CS Prototypical Application SWS 1 : OU-youtube O 1 :Purp O 1 :Env SWS 2 : entertain-youtube O 2 :Purp O 2 :Env SWS 3 : open-video O 3 :Purp O 3 :Env SWS 4 : bbc-backstage O 4 :Purp O 4 :Env M 6 2 ={v 1 , v 2 } SWS 5 : mobile-youtube O 5 :Purp O 5 :Env CS 2 Environment Space CS 1 Purpose Space SWS 6 : get-video-request M 6 1 ={v 1, v 2 , v 3 } WS 1 : OU-youtube WS 2 : entertain-youtube WS 3 : open-video WS 4 : bbc-backstage WS 5 : mobile-youtube {(p 1 *information, p 2 *education, p 3 *leisure)} = CS 1 {(p 4 *resolution, p 5 *bandwidth)} = CS 2
    • 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)
    • 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
    • SWS Mediation based on CS Prototypical Application SWS 1 : OU-youtube O 1 :Purp O 1 :Env SWS 2 : entertain-youtube O 2 :Purp O 2 :Env SWS 3 : open-video O 3 :Purp O 3 :Env SWS 4 : bbc-backstage O 4 :Purp O 4 :Env M 6 2 ={v 1 , v 2 } SWS 5 : mobile-youtube O 5 :Purp O 5 :Env CS 2 Environment Space CS 1 Purpose Space SWS 6 : get-video-request M 6 1 ={v 1, v 2 , v 3 } WS 1 : OU-youtube WS 2 : entertain-youtube WS 3 : open-video WS 4 : bbc-backstage WS 5 : mobile-youtube
      • Requests (WSMO Goals) defined through AJAX-based UI
      • Requests consists of:
        • Input parameters: set of keywords (free text)
        • Assumption: defined through vectors (measurements describing purpose and environment)
      • Similarity-based discovery of most suitable service based on WSMO mediator
    • DEMO SWS 1 : OU-youtube O 1 :Purp O 1 :Env SWS 2 : entertain-youtube O 2 :Purp O 2 :Env SWS 3 : open-video O 3 :Purp O 3 :Env SWS 4 : bbc-backstage O 4 :Purp O 4 :Env M 6 2 ={v 1 , v 2 } SWS 5 : mobile-youtube O 5 :Purp O 5 :Env CS 2 Environment Space CS 1 Purpose Space SWS 6 : get-video-request M 6 1 ={v 1, v 2 , v 3 } WS 1 : OU-youtube WS 2 : entertain-youtube WS 3 : open-video WS 4 : bbc-backstage WS 5 : mobile-youtube
      • 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
      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).
      Conclusions Discussion and Summary
    • Thank you!
      • E-mail: [email_address]
      • Web: http://people.kmi.open.ac.uk/dietze