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Using semantic annotation of web services for analyzing

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Presented at ICWS 2012

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Using semantic annotation of web services for analyzing

  1. 1. Information Diffusion in Web Services NetworksShahab Mokarizadeh , Royal Institute of Technology (KTH) , Sweden Peep Küngas, University of Tartu (UT) , Estonia Mihhail Matskin , Royal Institute of Technology (KTH) , Sweden Marco Crasso, Marcelo Campo, Alejandro Zunino , UNICEN University, Argentina Contact: shahabm@kth.se1
  2. 2. Outline  Background of Information Flow Analysis  Roadmap and Computational Model  Web service Annotation  Web service Categorization  Experimental Results  Discussion & Conclusion2
  3. 3. Background – Information Diffusion Information Diffusion: the communication of knowledge over time among members of a social system It shows intrinsic properties of real-world phenomenon. Already studied in the context of: biosphere, microblogs,publication citation, … where a network structure present.3
  4. 4. Information Diffusion among Web service DomainsObservation: Services published in the Web form a conceptual ecology of knowledge where information is shared and flows along input and output parameters of service operations.Case-study: How Web services in different commodities have been designed from information exchange perspective?  Introducing value-add Web services  Web service adoption spots4
  5. 5. Roadmap 1 • Semantically annotation of Web services 2 • Assign Web services to respective categories 3 • Construct Web service network 4 • Compute information flow matrix • Matrix Analysis 55
  6. 6. 1-Web service Annotation-Only semantic annotations of basic elements of input and outputparameters of Web service Operations-SAWSDL annotation model-We exploit our Semi-automated ontology learning method whichrelies on lexico-syntactic patterns “Ontology Learning for Cost-Effective Large-Scale Semantic Annotation of Web Service Interfaces”. EKAW 2010:pp. 401-410 Image from : Web Services and6 Security,1/17/2006 ,Marco Cova
  7. 7. Tax and Customs Board service Output message content fragment7
  8. 8. Business Registry service Input message content fragment8
  9. 9. A Business Registry service9 Output message content fragment
  10. 10. Registry of Economic Activities Service Output message content fragment10
  11. 11. 2-Web service CategorizationA category (a.k.a. commodity) describes a general kind of a servicethat is provided, for example “B2B” , “Health”, “E-Commerce”, etc.Each Web service could belong to multiple categories !Standard Software Taxonomy e.g. UNSPSC: http://www.unspsc.org/We use Classifier : "AWSC: An approach to Web Service classificationbased on machine learning techniques“, Inteligencia Artificial, ISSN 1137-3601, vol.12, no. 37, pp. 25-36, Asociación Española para la Inteligencia Artificial, Valencia, España.2008. UNSPSC Instant messaging Calendar and scheduling Adventure games Mobile operator specific Internet directory services Medical software11 Music or sound editing Video conferencing software
  12. 12. 3-Web service Network Construction1- Present annotated Web services as bipartite (2-mode) graph2- Create Semantic Network (1-mode graph)3- Create Weighted Category Network using Semantic network12
  13. 13. Bipartite Web Service Network13
  14. 14. Bipartite Web Service Network (categorized)14
  15. 15. Network Transformation Semantic Network Category NetworkPropagate the categories to semantic Ds, Dt : category nodesnodes , Cu: semantic node , Label each category edge with weights:qk: weight of node in category k Q u  q1 ,..qk ., qn  u ,v ( Ds , Dt )  qu , s .qv,t frequency of Cu in Ds 15 qs  n  frequency of Cu in Di W ( Ds , Dt )   edge ( u ,v ) u ,v ( D s , Dt ) i 1
  16. 16. 4-Normalizing Weights (Z-score) Edge category weight W(Di,Dj) : Wi,j Sum of all weights of all links from category i: W i *   W ( Di , D j ) j Sum of all weights of all links to category j: W* j   W ( Di , D j ) i Sum of weights of all categories: W   W ( Di , D j ) i, j Expected weights from category i to category j : Wi*  W* j W Normalize category weights (Z-Score): Wi*  W* j Wi*  W* j i , j  (Wi , j  ) W W16
  17. 17. Matrix of Information flowMatrix of information flow between pair of categories: 1,1  1, j  1,n             i ,1  i , j  i ,n            n ,1  n, j   n,n   A high proximity (Φ i j) between categories i and j reveals a strongtendency for semantic concepts associated to category j to be resultedfrom invocation of services which take semantic concepts associated tocategory i.17
  18. 18. 5-Experimental Settings 27000 public Web services (WSDLs) (collected 2005-2011) Semantic Annotation  Lexico-syntactic based ontology learning  Annotation accuracy: Precision= 31% , Recall= 19% Categorization  AWSC Classifier  Training dataset: 1500 WSDLs  Categorization Accuracy: 91% 18
  19. 19. Excerpt of Identified Service Categories Category Category1-Communications server 11-Network operation system2-Instant messaging 12-Database management system3-Adventure games 13-Analytical or scientific4-Internet directory services 14-Portal server5-Music or sound editing 15-Foreign language software6-Calendar and scheduling 16-Procurement software7-Mobile operator specific 17-Inventory management software8-Medical software 18-Dictionary software9-Video conferencing 19-Fax software10-Map creation software 20-Object oriented database management 19
  20. 20. Visualization of Matrix of Information Flow20
  21. 21. Information Exchange Patterns - 1: Self-Referential Pattern: A category mainly provides inputs for its own services and consumes mostly the information provided by itself (i.e. self contained).  Appear in diagonal of matrix  Categories: Financial Analysis Software, Web Platform Development Software, Map Creation Software, Video Conferencing Software and Accounting Software  The API-s exposed by these Web services exploit frequently domain-specific concepts as input and output elements21
  22. 22. Information Exchange Patterns - 2: Outside main diagonal:-Foreign Language category , Presentation category-Financial Analysis category , Enterprise Resource Planning category Least volume of information flow: -Video Conferencing software and Financial Analysis software22
  23. 23. Threats to Validity The presented model heavily relies of accuracy of underlying semantic annotation and matching scheme ! The examined Web services account only for small proportion of existing ones on the Web! The collection of Web services’ interface descriptions may also suffer from unintentional preference toward some specific categories. In the absence of timing factor our analysis is rather static analysis of information flow23
  24. 24. Conclusion and Future Work The presented approach can discover information exchange patterns. In general our approach is applicable to any other kind of machine understandable APIs, not just WSDLs, !Future work:To examine how presence of service composition or mashups influences the information exchange patternRecommending value-add Web services based on identified information exchange patterns and Web service network properties24
  25. 25. Thanks! Questions Please!25
  26. 26. Partial Category Weight for Edge (Ds,Dt) : u ,v ( Ds , Dt )  qu , s .qv,tAugmented Category Weight for Edge (Ds, Dt): W ( Ds , Dt )   edge ( u ,v ) u ,v ( D s , Dt )26
  27. 27. Ontology Learning for Information Elicitation Web service Annotation1 Term Extraction Syntactic Refinement Ontology DiscoveryOntology Learning Input: Pattern-based - Message Part names of input/output Semantic Analysis parameters Term Disambiguation - XML Schema leaf element names of complex types Class and Relation Determination Ontology Organization Adding Relations[1] ”Ontology Learning for Cost-Effective Large-scale SemanticAnnotation of XML Schemas and Web Service Interfaces". in Porc.EKAW 2010, LNAI 6317,pp.401-410, 2010 Reference27 Ontology

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