Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Correlation consistency constrained matrix completion for web service tag refinement

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

With the permeation of web 2.0, large-scale web services with tags are easily available on websites. However, the noisy and incomplete correspondence between web services and tags impedes the tag related web service applications. To address this challenge, a Matrix Completion based Web Service Tag Refinement (MCWSTR) framework is proposed. Firstly, the MCWSTR framework naturally formulates the web service tag refinement problem as a Correlation Consistency Constrained Matrix Completion (C3MC) problem, which jointly model content correlation consistency and tag correlation consistency among web services and tags. Secondly, the MCWSTR framework employs an efficient Fixed Point Iterative algorithm based on Operator Splitting technique to solve the C3MC problem, Experimental results on the real-world web services collection show the encouraging performance of our proposed MCWSTR framework.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Ding Z, Lei D, Yan J (2010) A Web service discovery method based on tag, International conference on complex, intelligent and software intensive systems, pp 404–408

  2. Chen L, Hu L, Zheng Z et al (2011) WTCluster: utilizing tags for web services clustering. In: Kappel G, Maamar Z, Motahari-Nezhad HR (eds) LNCS, vol 7084. Springer, Heidelberg, pp 204–218

    Google Scholar 

  3. Fernandez A, Hayes C, Loutas N et al (2008) Closing the service discovery gap by collaborative tagging and clustering techniques, International workshop on service matchmaking and resource retrieval in the semantic web, pp 115–128

  4. Loutas N, Peristeras V, Tarabanis K (2011) Towards a reference service model for the web of services. Data Knowl Eng 70(9):753–774

    Article  Google Scholar 

  5. Loutas N, Peristeras V, Zeginis D et al (2012) The Semantic Service Search Engine (S3E). J Intell Inf Syst 38(3):645–668

    Article  Google Scholar 

  6. Chen L, Wang Y, Yu Q et al (2013) WT-LDA: user tagging augmented LDA for web service clustering. International conference on service oriented computing, Berlin, pp 1–15

    Google Scholar 

  7. Katakis I, Pallis G, Dikaiakos M et al (2012) Automated tagging for the retrieval of software resources in grid and cloud infrastructures. IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, pp 628–635

    Google Scholar 

  8. Azmeh Z, Falleri J, Huchard M et al (2011) Automatic web service tagging using machine learning and wordnet synsets. In: Filipe IJ, Cordeiro J (eds) WEBIST 2010, vol 25. LNBIP, Spain, pp 46–59

    Google Scholar 

  9. Fang L, Wang L, Li M et al (2012) Towards automatic tagging for web services, IEEE International conference on web services, pp 528–535

  10. Zhao R, Grosky W (2002) Narrowing the semantic gap improved text-based web document retrieval using visual features. IEEE Trans Multimed 4(2):189–200

    Article  Google Scholar 

  11. Zhu G, Yan S, Ma Y (2010) Image tag refinement towards low-rank, content-tag prior and error sparsity, ACM Multimedia, pp 461–470

  12. Chen L, Yang G, Zhu W et al (2013) Clustering facilitated web services discovery model based on supervised term weighting and adaptive metric learning. Int J Web Eng Technol 8(1):58–80

    Article  Google Scholar 

  13. Liu J, Zhang Y, Li Z et al (2013) Correlation consistency constrained probabilistic matrix factorization for social tag refinement. Neurocomputing 119:3–9

    Article  Google Scholar 

  14. Wu L, Jin R, Jain A (2013) Tag completion for image retrieval. IEEE Trans Pattern Anal Mach Intell 35(3):716–727

    Article  Google Scholar 

  15. Cai JF, Candes E, Shen Z (2010) A singular value thresholding algorithm for matrix completion. SIAM J Optim 20(4):1956–1982

    Article  MathSciNet  MATH  Google Scholar 

  16. Ma S, Goldfarb D, Chen L (2011) Fixed point and bregman iterative methods for matrix rank minimization. Math Program Ser A 128(1):321–353

    Article  MathSciNet  MATH  Google Scholar 

  17. Boyd S, Vandenberghe L (2009) Convex optimization, Cambridge University Press, Cambridge

  18. Combettes SP, Wajs V (2005) Signal recovery by proximal forward-backward splitting, multi-scale modeling and simulation. SIAM Interdiscip J 4:1168–1200

    MathSciNet  MATH  Google Scholar 

  19. Mirsky L (1975) A trace inequality of john von neumann. Mon Math 79(4):303–306

    Article  MathSciNet  MATH  Google Scholar 

  20. Wang C, Jing F, Zhang L et al (2007) Content-based image annotation refinement, IEEE Conference on computer vision and pattern recognition, pp 123–130

  21. Huiskes M, Lew M (2008) The MIR Flickr retrieval evaluation, ACM International conference on multimedia information retrieval, pp 39–43

  22. Liu D, Hua X S, Wang M, Zhang H J (2010) Image retagging, ACM International conference on multimedia, pp 491–500

Download references

Acknowledgments

This work is supported by the National Natural Science Foundation (grant numbers 61272084, 61201163, 61272422 and 61373137); the Natural Science Foundation of Jiangsu Province (grant numbers BK2011754, BK20130096); the Key University Natural Science Research Program of Jiangsu (grant number 11KJA520002) and the Research Fund for the Doctoral Program of High Education (grant numbers 20113223110003, 20093223120001).

Author information

Authors and Affiliations

  1. School of Computer, Nanjing University of Posts and Telecommunications, Nanjing, 210003, People’s Republic of China

    Lei Chen, Zhengyu Chen, Fu Xiao & Jianyue Shi

  2. Key Laboratory of Broadband Wireless Communication and Sensor Network Technology, Nanjing University of Posts and Telecommunications, Nanjing, 210003, People’s Republic of China

    Lei Chen & Geng Yang

  3. School of Information Technology, Jinling Institute of Technology, Nanjing, 211169, People’s Republic of China

    Zhengyu Chen

Authors
  1. Lei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Geng Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhengyu Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fu Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jianyue Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lei Chen.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, L., Yang, G., Chen, Z. et al. Correlation consistency constrained matrix completion for web service tag refinement. Neural Comput & Applic 26, 101–110 (2015). https://doi.org/10.1007/s00521-014-1704-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-014-1704-z

Keywords

Search

Navigation