Skip to main content

Service discovery acceleration with hierarchical clustering

Abstract

This paper presents an efficient Web Service Discovery approach based on hierarchical clustering. Conventional web service discovery approaches usually organize the service repository in a list manner, therefore service matchmaking is performed with linear complexity. In this work, services in a repository are clustered using hierarchical clustering algorithms with a distance measure from an attached matchmaker. Service discovery is then performed over the resulting dendrogram (binary tree). In comparison with conventional approaches that mostly perform exhaustive search, we show that service-clustering method brings a dramatic improvement on time complexity with an acceptable loss in precision.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Notes

  1. World Wide Web Consortium

  2. Though other formats are technically acceptable as well

  3. http://projects.semwebcentral.org/projects/owls-tc/

  4. prefix sumo= http://127.0.0.1/ontology/SUMO.owl%23

  5. http://www-ags.dfki.uni-sb.de/%7Eklusch/s3/

References

  • Blake, B., Cabral, L., König-Ries, B. (2012). Semantic web services: Advancement through evaluation: Springer.

  • Christensen, E., Curbera, F., Meredith, G., Weerawarana, S., et al. (2001). Web services description language (wsdl) 1.1.

  • De Bruijn, J., Lausen, H., Polleres, A., Fensel, D. (2006). The web service modeling language wsml: An overview. In: The Semantic Web: Research and Applications, (pp. 590–604): Springer.

  • Elgazzar, K., Hassan, A.E., Martin, P. (2010). Clustering wsdl documents to bootstrap the discovery of web services. In: Web Services (ICWS), 2010 IEEE International Conference on, pp. 147–154. IEEE.

  • Everitt, B.S., Landau, S., Leese, M., Stahl, D. (2001). Hierarchical clustering. Cluster Analysis, 5th Edition.

  • Fernández, A., Cong, Z., Baltá, A. (2012). Bridging the gap between service description models in service matchmaking. Multiagent and Grid Systems, 8 (1), 83–103.

    Google Scholar 

  • Fernandez, A., Hayes, C., Loutas, N., Peristeras, V., Polleres, A., Tarabanis, K. (2008). Closing the service discovery gap by collaborative tagging and clustering techniques. In: 7th International Semantic Web Conference, ISWC, (pp. 115–128).

  • Fung, B.C., Wang, K., Ester, M. (2003). Hierarchical document clustering using frequent itemsets. In: Proceedings of SIAM international conference on data mining.

  • Hartigan, J.A., & Wong, M.A. (1979). Algorithm as 136: A k-means clustering algorithm. Journal of the Royal Statistical Society. Series C (Applied Statistics), 28 (1), 100–108.

    Google Scholar 

  • Kiefer, C., & Bernstein, A. (2008). The creation and evaluation of isparql strategies for matchmaking. In: The Semantic Web: Research and Applications, (pp. 463–477): Springer.

  • Klusch, M., Fries, B., Sycara, K. (2006). Automated semantic web service discovery with owls-mx. In: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems, pp. 915–922. ACM.

  • Klusch, M., & Kapahnke, P. (2010). isem: Approximated reasoning for adaptive hybrid selection of semantic services. In: The semantic web: Research and applications, (pp. 30–44): Springer.

  • Kopecky, J., Vitvar, T., Bournez, C., Farrell, J. (2007). Sawsdl: Semantic annotations for wsdl and xml schema. Internet Computing, IEEE, 11 (6), 60–67.

    Article  Google Scholar 

  • Larsen, B., & Aone, C. (1999). Fast and effective text mining using linear-time document clustering. In: Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining pp. 16–22. ACM.

  • Li, Y., Bandar, Z.A., McLean, D. (2003). An approach for measuring semantic similarity between words using multiple information sources. Knowledge and Data Engineering, IEEE Transactions on, 15(4), 871–882.

    Article  Google Scholar 

  • Liu, W., & Wong, W. (2009). Web service clustering using text mining techniques. International Journal of Agent-Oriented Software Engineering, 3(1), 6–26.

    Article  Google Scholar 

  • Manning, C.D., Raghavan, P., Schütze, H. (2008). Introduction to information retrieval, vol. 1: Cambridge University Press Cambridge.

  • Martin, D., Paolucci, M., McIlraith, S., Burstein, M., McDermott, D., McGuinness, D., Parsia, B., Payne, T., Sabou, M., Solanki, M., et al. (2005). Bringing semantics to web services: The owl-s approach. In: Semantic Web Services and Web Process Composition, (pp. 26–42): Springer.

  • Miller, G.A. (1995). Wordnet: a lexical database for english. Communications of the ACM, 38(11), 39–41.

    Article  Google Scholar 

  • NAICS-Association, et al. (2003). Naics-north american industry classification system.

  • Paolucci, M., Kawamura, T., Payne, T.R., Sycara, K. (2002). Semantic matching of web services capabilities. In: The Semantic WebISWC 2002, (pp. 333–347): Springer.

  • Pedrinaci, C., Liu, D., Maleshkova, M., Lambert, D., Kopecky, J., Domingue, J. (2010). iserve: a linked services publishing platform. In: CEUR workshop proceedings, vol. 596.

  • Sheth, A., Verma, K., Miller, J., Rajasekaran, P. (2005). Enhancing web service descriptions using wsdl-s. Research-Industry Technology Exchange at EclipseCon.

  • Slonim, N., & Tishby, N. (2000). Document clustering using word clusters via the information bottleneck method. In: Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval pp. 208–215. ACM.

  • Steinbach, M., Karypis, G., Kumar, V., et al. (2000). A comparison of document clustering techniques. In: KDD workshop on text mining, vol. 400, (pp. 525–526): Boston.

  • Sundberg, R. (1974). Maximum likelihood theory for incomplete data from an exponential family. Scandinavian Journal of Statistics, 49–58.

  • Yue, P. (2013). Automatic service composition. In: Semantic Web-based Intelligent Geospatial Web Services, (pp. 21–25): Springer.

  • Zhao, Y., Karypis, G., Fayyad, U. (2005). Hierarchical clustering algorithms for document datasets. Data Mining and Knowledge Discovery, 10(2), 141–168.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Alberto Fernandez.

Additional information

Work supported by the Spanish Government through grants TIN2009-13839-C03-02 (co-funded by Plan E), CSD2007-0022 (CONSOLIDER-INGENIO 2010) and TIN2012-36586-C03-02

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Cong, Z., Fernandez, A., Billhardt, H. et al. Service discovery acceleration with hierarchical clustering. Inf Syst Front 17, 799–808 (2015). https://doi.org/10.1007/s10796-014-9525-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10796-014-9525-2

Keywords

  • Service discovery
  • Hirerachical clustering
  • Service matchmaking