The KIPS Transactions:PartD (정보처리학회논문지D)

Korea Information Processing Society (한국정보처리학회)
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Discovery Methods of Similar Web Service Operations by Learning Ontologies

온톨로지 학습에 의한 유사 웹 서비스 오퍼레이션 발견 방법

  • 이용주 (경북대학교 이공대학 컴퓨터정보학부)
  • Received : 2010.10.13
  • Accepted : 2010.12.14
  • Published : 2011.04.30
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Abstract

To ensure the successful employment of semantic web services, it is essential that they rely on the use of high quality ontologies. However, building such ontologies is difficult and costly, thus hampering web service deployment. This study automatically builds ontologies from WSDL documents and their underlying semantics, and presents discovery methods of similar web service operations using these ontologies. The key ingredient is techniques that cluster parameters in the collection of web services into semantically meaningful concepts, and capture the hierarchical relationships between the words contained in the tag. We implement an operation retrieval system for web services. This system finds out a ranked set of similar operations using a novel similarity measurement method, and selects the most optimal operation which satisfies user's requirements. It can be directly used for the web services composition.

시맨틱 웹 서비스 기술의 성공을 보장하기 위해서는 품질 좋은 온톨로지의 사용이 필수적이다. 하지만 온톨로지 사용의 중요성에도 불구하고 현재 웹 서비스를 위한 온톨로지는 거의 존재하지 않으며 이들의 구축도 쉬운 일이 아니다. 이러한 문제는 오늘날 웹 서비스의 확산과 발전을 가로막는 큰 저해 요인이 되고 있다. 본 논문에서는 웹 서비스를 개발할 때 자동으로 생성되는 WSDL 문서만 가지고 항목 간 숨어있는 시맨틱 정보를 찾아내어 온톨로지를 자동 구축하고, 이를 이용한 유사 웹 서비스 오퍼레이션 발견 방법을 제안한다. 핵심 내용은 WSDL 입출력 항목들로부터 의미적으로 같은 개념들을 묶고, 각 항목들 간의 계층관계를 형성하여 자동적으로 시맨틱 온톨로지를 구축한다. 그리고 새로운 유사도 측정 방법을 통해 우선순위별 유사 오퍼레이션을 발견하며, 발견된 오퍼레이션들 중 가장 적합한 오퍼레이션을 선택하여 웹 서비스 조합에 직접 활용할 수 있는 웹 서비스 오퍼레이션 검색 시스템을 구현한다.

Keywords

References

  1. Wikipedia, Semantic Web Services, http://en.wikipedia.org/wiki/Semantic_Web_Services.
  2. B. Khalid and B. Marco, "Ontology-Based Description andDiscovery of Business Processes," BPMDS 2009 and EMMSAD 2009, LNBIP 29, pp.85-98, 2009.
  3. S. Nathalie, et al., "Service Finder: Realizing Web Service Discovery at Web Scale(First Design of Service-Finder as a Whole)," http://www.service-finder.eu/, June, 2008.
  4. A. P. Sheth, K. Gomadam, and A. Ranabahu, "Semantics Enhanced Services: METEOR-S, SAWSDL and SAREST," IEEE Data Engineering Bulletin, Vol.31, No.3, pp.8-12, September, 2008.
  5. K. Belhajjame, S. M. Embury, N. W. Paton, R. Stevens, and A. C. Goble, "Automatic Annotations of Semantic Web Services Based on Workflow Definitions," ACM Transactions on the Web 2 (2): pp.1-34, April, 2008.
  6. E. Sirin, B. Parsia, and J. Hendler, "Composition-driven Filtering and Selection of Semantic Web Service," In AAAI Spring Symposium on Semantic Web Services, 2004.
  7. K. Verma, K. Gomadam, A. Sheth, J. Miller, and Z. Wu, "The METEOR-S Approach for Configuring and Executing Dynamic Web Processes," Technical Report, LSDIS Lab, University of Georgia, 2005.
  8. T. Vitvar, M. Zaremba, M. Moran, M. Zaremba, and D. Fensel, "SESA: Emerging Technology for Service-Centric Environments," IEEE Software, Vol.24, No.6, pp.56-67, 2007. https://doi.org/10.1109/MS.2007.178
  9. J. Kopecky, T. Vitvar, C. Bournez, and J. Jarrell, "SAWSDL: Semantic Annotations for WSDL and XML Schema," IEEE Internet Computing, Vol.11, No.6, pp.60-67, November/December, 2007. https://doi.org/10.1109/MIC.2007.134
  10. T. Syeda-Mahmood, G. Shah, R. Akkiraju, A. Lvan, and R. Goodwin, "Searching Service Repositories by Combining Semantic and Ontological Matching," Proceedings of IEEE International Conference on Web Services(ICWS), 2005. https://doi.org/10.1109/ICWS.2005.102
  11. A. Hess and N. Kushmerick, "Learning to Attach Metadata to Web Services," In Proceedings of the International Semantic Web Conference, 2003.
  12. X. Dong, A. Halevy, J. Madhavan, E. Nemes, and J. Zhang, "Similarity Search for Web Services," In Proceedings of VLDB, 2004.
  13. M. Sabou, C. Wroe, C. Goble, and H. Stuckenschmidt, "Learning Domain Ontologies for Semantic Web Service Descriptions," Journal of Web Semantics, 3(4), 2005.
  14. H. Guo, A. Ivan, R. Akkiraju, and R. Goodwin, "Learning Ontologies to Improve the Quality of Automatic Web Service Matching," Proceedings of IEEE International Conference on Web Services(ICWS), 2007. https://doi.org/10.1109/ICWS.2007.114
  15. 이용주, "반자동 웹 서비스 조합을 위한 WS-BPEL과 OWL-S의 융합 시스템," 정보처리학회논문지D 제15-D권 제4호, pp. 569-580, 2008. https://doi.org/10.3745/KIPSTD.2008.15-D.4.569
  16. G. Miller and C. Fellbaum, "WordNet," http://wordnet.princeton.edu
  17. G. Salton and C. Buckley, "Term Weighting Approaches in Automatic Text Retrieval," Information Processing and Management, 24(4), 1988.
  18. R. Agrawal, T. Imielinski, and A. Swami, "Mining Association Rules between Sets of Items in Large Databases," Proceedings of the 1993 ACM-SIGMOD International Conference Management of Data, 1993.
  19. D. Braga, A. Campi, S. Ceri, M. Klemetinen, and P. Lanzi, "Discovering Interesting Information in XML Data with Association Rules," SAC, Proceedings of the 2003, ACM Symposium on Applied Computing Table of Contents, pp. 450-454, 2003.
  20. R. Agrawal and R. Srikant, "Fast Algorithm for Mining Associations Rules," In Proceedings of the 20th VLDB Conference, Santiage, Chile, Sept., 1994.
  21. http://niels.drni.de/s9y/pages/clusterlib.html
  22. http://www.xmethods.net
  23. P. Velardi, P. Fabriani, M. Missikoff, "Using Text Processing Techniques to Automatically Enrich a Domain Ontology," Proceedings of the ACM International Conference on Formal Ontology in Information Systems, 2001.
  24. http://crftagger.sourceforge.net/
  25. http://opennlp.sourceforge.net/
  26. F. Coenen, "The LUCS-KDD Apriori-T Association Rule Mining Algorithm," http://www.csc.liv.ac.uk/-frans/KDD/Software/Apriori-T/aprioriT.html, 2004.