Skip to main content

Advertisement

SpringerLink
Log in
Menu
Find a journal Publish with us
Search
Cart
Book cover

International Conference on Service-Oriented Computing

ICSOC 2012: Service-Oriented Computing pp 142–157Cite as

  1. Home
  2. Service-Oriented Computing
  3. Conference paper
Extending Enterprise Service Design Knowledge Using Clustering

Extending Enterprise Service Design Knowledge Using Clustering

  • Marcus Roy20,21,
  • Ingo Weber21,22 &
  • Boualem Benatallah21 
  • Conference paper
  • 2210 Accesses

  • 1 Citations

Part of the Lecture Notes in Computer Science book series (LNPSE,volume 7636)

Abstract

Automatically constructing or completing knowledge bases of SOA design knowledge puts traditional clustering approaches beyond their limits. We propose an approach to amend incomplete knowledge bases of Enterprise Service (ES) design knowledge, based on a set of ES signatures. The approach employs clustering, complemented with various filtering and ranking techniques to identify potentially new entities. We implemented and evaluated the approach, and show that it significantly improves the detection of entities compared to a state-of-the-art clustering technique. Ultimately, extending an existing knowledge base with entities is expected to further improve ES search result quality.

Keywords

  • Directed Acyclic Graph
  • Service Design
  • Hierarchical Agglomerative Cluster
  • Naming Convention
  • Name Entity Recognition

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Download conference paper PDF

References

  1. Agichtein, E., Gravano, L.: Snowball: Extracting Relations From Large Plain-Text Collections. In: DL 2000, pp. 85–94. ACM, New York (2000)

    CrossRef  Google Scholar 

  2. Bennett, S.G., Gee, C., Laird, R., Manes, A.T., Schneider, R., Shuster, L., Tost, A., Venable, C.: SOA Governance: Governing Shared Services On-Premise and in the Cloud. Prentice Hall (2011)

    Google Scholar 

  3. Brauer, F., Huber, M., Hackenbroich, G., Leser, U., Naumann, F., Barczynski, W.M.: Graph-Based Concept Identification and Disambiguation for Enterprise Search. In: WWW 2010, pp. 171–180. ACM, New York (2010)

    Google Scholar 

  4. Chakaravarthy, V.T., Gupta, H., Roy, P., Mohania, M.: Efficiently Linking Text Documents With Relevant Structured Information. In: VLDB 2006, pp. 667–678 (2006)

    Google Scholar 

  5. Chandel, A., Nagesh, P., Sarawagi, S.: Efficient Batch Top-k Search for Dictionary-based Entity Recognition. In: ICDE 2006, p. 28 (April 2006)

    Google Scholar 

  6. Chieu, H.L., Ng, H.T.: Named Entity Recognition: A Maximum Entropy Approach Using Global Information. In: COLING 2002, USA, pp. 1–7 (2002)

    Google Scholar 

  7. Dong, X., Halevy, A., Madhavan, J., Nemes, E., Zhang, J.: Similarity Search for Web Services. In: VLDB 2004, pp. 372–383. VLDB Endowment (2004)

    Google Scholar 

  8. Falkl, J., Laird, R., Carrato, T., Kreger, H.: IBM Advantage for SOA Governance Standards (July 2009), http://www.ibm.com/developerworks/webservices/library/ws-soagovernanceadv/index.html

  9. Hassell, J., Aleman-Meza, B., Budak Arpinar, I.: Ontology-Driven Automatic Entity Disambiguation in Unstructured Text. In: Cruz, I., Decker, S., Allemang, D., Preist, C., Schwabe, D., Mika, P., Uschold, M., Aroyo, L.M. (eds.) ISWC 2006. LNCS, vol. 4273, pp. 44–57. Springer, Heidelberg (2006)

    CrossRef  Google Scholar 

  10. Heß, A., Kushmerick, N.: Learning to Attach Semantic Metadata to Web Services. In: Fensel, D., Sycara, K., Mylopoulos, J. (eds.) ISWC 2003. LNCS, vol. 2870, pp. 258–273. Springer, Heidelberg (2003)

    CrossRef  Google Scholar 

  11. Irmak, U., Kraft, R.: A Scalable Machine-Learning Approach for Semi-Structured Named Entity Recognition. In: WWW 2010, pp. 461–470. ACM, USA (2010)

    Google Scholar 

  12. Karypis, G., Han, E.-H., Kumar, V.: Chameleon: Hierarchical Clustering Using Dynamic Modeling. Computer 32(8), 68–75 (1999)

    CrossRef  Google Scholar 

  13. Malinverno, P.: Service-oriented architecture craves governance (October 2006), http://www.gartner.com/DisplayDocument?id=488180

  14. Oldham, N., Thomas, C., Sheth, A., Verma, K.: METEOR-S Web Service Annotation Framework with Machine Learning Classification. In: Cardoso, J., Sheth, A. (eds.) SWSWPC 2004. LNCS, vol. 3387, pp. 137–146. Springer, Heidelberg (2005)

    CrossRef  Google Scholar 

  15. Roy, M., Suleiman, B., Schmidt, D., Weber, I., Benatallah, B.: Using SOA Governance Design Methodologies to Augment Enterprise Service Descriptions. In: Mouratidis, H., Rolland, C. (eds.) CAiSE 2011. LNCS, vol. 6741, pp. 566–581. Springer, Heidelberg (2011)

    CrossRef  Google Scholar 

  16. SAP. Governance for Modeling and Implementing Enterprise Services at SAP (April 2007), http://www.sdn.sap.com/irj/sdn/go/portal/prtroot/docs/library/uuid/f0763dbc-abd3-2910-4686-ab7adfc8ed92

  17. Saquete, E., Ferrández, O., Ferrández, S., Martínez-Barco, P., Muñoz, R.: Combining Automatic Acquisition of Knowledge With Machine Learning Approaches for Multilingual Temporal Recognition and Normalization. In: IS 2008, pp. 3319–3332 (2008)

    Google Scholar 

  18. Voorhees, E.M.: The Effectiveness and Efficiency of Agglomerative Hierarchic Clustering in Document Retrieval. PhD thesis, Ithaca, NY, USA (1986)

    Google Scholar 

  19. Watson, B.W.: A New Algorithm for the Construction of Minimal Acyclic DFAs. Science of Computer Programming 48(2-3), 81–97 (2003)

    CrossRef  MathSciNet  MATH  Google Scholar 

  20. Wang, W., Xiao, C., Lin, X., Zhang, C.: Efficient Approximate Entity Extraction With Edit Distance Constraints. In: SIGMOD 2009, pp. 759–770. ACM, USA (2009)

    CrossRef  Google Scholar 

  21. Willett, P.: Recent Trends in Hierarchic Document Clustering: A Critical Review. Information Processing and Management 24(5), 577–597 (1988)

    CrossRef  Google Scholar 

  22. Witten, I.H., Moffat, A., Bell, T.C.: Managing Gigabytes: Compressing and Indexing Documents and Images. Morgan Kaufmann, San Francisco (1999)

    Google Scholar 

  23. Zamir, O., Etzioni, O., Madani, O., Karp, R.: Fast and Intuitive Clustering of Web Documents. In: Knowledge Discovery and Data Mining, pp. 287–290 (1997)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. SAP Research, Sydney, Australia

    Marcus Roy

  2. School of Computer Science & Engineering, University of New South Wales, Australia

    Marcus Roy, Ingo Weber & Boualem Benatallah

  3. Software Systems Research Group, NICTA, Sydney, Australia

    Ingo Weber

Authors
  1. Marcus Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ingo Weber
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Boualem Benatallah
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Faculty of ICT, Swinburne University of Technology, John Street, 3122, Hawthorn, VIC, Australia

    Chengfei Liu

  2. IBM Almaden Research Center, 650 Harry Road, 95120, San Jose, CA, USA

    Heiko Ludwig

  3. LIMOS - UMR 6158, Blaise Pascal University, Complexe scientifique des Cézeaux, 63177, Aubiere, France

    Farouk Toumani

  4. College of Computing and Information Sciences, Rochester Institute of Technology, 1 Lomb Memorial Drive, 14623, Rochester, NY, USA

    Qi Yu

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Roy, M., Weber, I., Benatallah, B. (2012). Extending Enterprise Service Design Knowledge Using Clustering. In: Liu, C., Ludwig, H., Toumani, F., Yu, Q. (eds) Service-Oriented Computing. ICSOC 2012. Lecture Notes in Computer Science, vol 7636. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34321-6_10

Download citation

  • .RIS
  • .ENW
  • .BIB
  • DOI: https://doi.org/10.1007/978-3-642-34321-6_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34320-9

  • Online ISBN: 978-3-642-34321-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Share this paper

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Search

Navigation

  • Find a journal
  • Publish with us

Discover content

  • Journals A-Z
  • Books A-Z

Publish with us

  • Publish your research
  • Open access publishing

Products and services

  • Our products
  • Librarians
  • Societies
  • Partners and advertisers

Our imprints

  • Springer
  • Nature Portfolio
  • BMC
  • Palgrave Macmillan
  • Apress
  • Your US state privacy rights
  • Accessibility statement
  • Terms and conditions
  • Privacy policy
  • Help and support

167.114.118.210

Not affiliated

Springer Nature

© 2023 Springer Nature