Discovering Plain-Text-Described Services Based on Ontology Learning

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8836)


In this paper, we present an approach to efficiently discover domain-specific services that are described by plain text over the Internet. Plain-text-described service advertisements account for the vast majority of service advertisements over the Internet, but current research rarely focuses on this area. To address this issue, we design a domain-ontology-based approach for automatic plain-text-described service discovery. This approach incorporates a plain-text-described service ontology for standard service description, a plaintext- described service discovery framework for domain-relevant service discovery and ontology learning, and a machine-learning-based model for ontologybased service functionality annotation. The experimental results show that this approach is able to efficiently discover more relevant plain-text-described services than other approaches.


service computing service discovery Web mining ontology learning focused crawler 


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  1. 1.
    Dong, H., Hussain, F.K., Chang, E.: Semantic Web Service Matchmakers: State of the Art and Challenges. Concurrency Computat. Pract. Exper. 25, 961–988 (2013)CrossRefGoogle Scholar
  2. 2.
    Gruber, T.R.: A Translation Approach to Portable Ontology Specifications. Knowledge Acquisition 5, 199–220 (1993)CrossRefGoogle Scholar
  3. 3.
    Dong, H., Hussain, F.K., Chang, E.: Ontology-Learning-Based Focused Crawling for Online Service Advertising Information Discovery and Classification. In: Liu, C., Ludwig, H., Toumani, F., Yu, Q. (eds.) Service Oriented Computing. LNCS, vol. 7636, pp. 591–598. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  4. 4.
    Dong, H., Hussain, F.K.: SOF: A Semi-Supervised Ontology-Learning-Based Focused Crawler. Concurrency Computat. Pract. Exper. 25, 1755–1770 (2013)CrossRefGoogle Scholar
  5. 5.
    Noor, T.H., Sheng, Q.Z., Alfazi, A., Ngu, A.H.H., Law, J.: CSCE: A Crawler Engine for Cloud Services Discovery on the World Wide Web. In: ICWS 2013, pp. 443–450. IEEE, New York (2013)Google Scholar
  6. 6.
    Zheng, H.-T., Kang, B.-Y., Kim, H.-G.: An Ontology-Based Approach to Learnable Focused Crawling. Inform. Sciences 178, 4512–4522 (2008)CrossRefGoogle Scholar
  7. 7.
    Su, C., Gao, Y., Yang, J., Luo, B.: An Efficient Adaptive Focused Crawler based on Ontology Learning. In: HIS 2005, pp. 73–78. IEEE, New York (2005)Google Scholar
  8. 8.
    Phuc, H.L., Gauch, S., Qiang, W.: Ontology-Based Focused Crawling. In: eKNOW 2009. IEEE, New York (2009)Google Scholar
  9. 9.
    Dong, H., Hussain, F.K.: Focused Crawling for Automatic Service Discovery, Annotation, and Classification in Industrial Digital Ecosystems. IEEE Trans. Ind. Electron. 58, 2106–2116 (2011)CrossRefGoogle Scholar
  10. 10.
    Rennie, J., McCallum, A.: Using Reinforcement Learning to Spider the Web Efficiently. In: ICML 1999, pp. 335–343. Morgan Kaufmann Publishers Inc., San Francisco (1999)Google Scholar
  11. 11.
    Cortes, C., Vapnik, V.: Support-Vector Networks. Machine Learning 20, 273–297 (1995)zbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.School of Computer Science and Information TechnologyRMIT UniversityAustralia
  2. 2.Decision Support and e-Service Intelligence Lab, Centre for Quantum Computation and Intelligent Systems, School of SoftwareUniversity of Technology SydneyAustralia

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