Abstract
In recent years, with the exponential growth of Web services, how to find the best Web services quickly, accurately and efficiently from the large Web services becomes an urgent problem in Web service discovery. Based on the previous work, we propose a semantic Web service discovery method based on LDA clustering. Firstly, the OWL-S Web service documents are parsed to obtain the document word vectors. Then these vectors are extended to make the documents more abundant of semantic information. Moreover, these vectors are modeled, trained and inferred to get the Document-Topic distribution, and the Web service documents are clustered. Finally, we search the Web service request records or the Web services clusters to find Web services that meet the requirements. Based on the data sets of OWLS-TC4 and hRESTS-TC3_release2, the experimental results show that our method (LDA plus semantic) has higher accuracy (13.48% and 9.97%), recall (37.39% and 24.26%), F-value (30.46% and 23.58%) when compared with VSM method and LDA method.
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Notes
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Latent Dirichlet allocation, https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation.
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References
Chakraborty, D., Perich, F., Avancha, S., et al.: DReggie: semantic service discovery for m-Commerce applications. In: 20th Symposium on Reliable Distributed Systems, pp. 28–31 (2001)
Zhang, C., Zhao, T., Li, W., et al.: Towards logic-based geospatial feature discovery and integration using web feature service and geospatial semantic web. Int. J. Geogr. Inf. Sci. 24(6), 903–923 (2010)
GarcÃa, J.M., Ruiz, D., Ruiz-Cortés, A.: Improving semantic Web services discovery using SPARQL-based repository filtering. Web Semant. Sci. Serv. Agents World Wide Web 17, 12–24 (2012)
Paolucci, M., Sycara, K.: Autonomous semantic web services. IEEE Internet Comput. 7(5), 34–41 (2003)
Deng, S.G., Yin, J.W., Li, Y., et al.: A method of semantic Web service discovery based on bipartite graph matching. Chin. J. Comput. 31(8), 1364–1375 (2008)
Bernstein, A., Kiefer, C.: Imprecise RDQL: towards generic retrieval in ontologies using similarity joins. In: Proceedings of the 2006 ACM Symposium on Applied Computing, pp. 1684–1689 (2006)
Wu, J., Wu, Z.H., Li, Y., et al.: Web service discovery based on ontology and similarity of words. China J. Comput. 28(4), 595–602 (2005)
Bianchini, D., De Antonellis, V., Melchiori, M., et al.: Semantic-enriched service discovery. In: Proceedings of 22nd International Conference on Data Engineering, p. 38 (2006)
Sangers, J., Frasincar, F., Hogenboom, F., et al.: Semantic web service discovery using natural language processing techniques. Expert Syst. Appl. 40(11), 4660–4671 (2013)
Amorim, R., Claro, D.B., Lopes, D., et al.: Improving Web service discovery by a functional and structural approach. In: 20111 IEEE International Conference on Web Services, pp. 411–418 (2011)
Paliwal, A.V., Shafiq, B., Vaidya, J., et al.: Semantics-based automated service discovery. IEEE Trans. Serv. Comput. 5(2), 260–275 (2012)
Klusch, M., Fries, B., Sycara, K.: OWLS-MX: a hybrid semantic web service matchmaker for OWL-S services. Web Semant. Sci. Serv. Agents World Wide Web 7(2), 121–133 (2009)
Jiang, J.J., Conrath, D.W.: Semantic similarity based on corpus statistics and lexical taxonomy. In: Proceedings of International Conference Research on Computational Linguistics (ROCLING X), pp. 1–15 (1997)
Blei, D., Ng, A., Jordan, M.: Generative probabilistic model for collections of discrete data such as text corpora. J. Mach. Learn. Res. 3, 993–1022 (2002)
Lamberti, P.W., Majtey, A.P., Borras, A., et al.: Metric character of the quantum Jensen-Shannon divergence. Phys. Rev. A 77(5), 1–8 (2008)
Niwattanakul, S., Singthongchai, J., Naenudorn, E., et al.: Using of Jaccard coefficient for keywords similarity. In: Proceedings of the International MultiConference of Engineers and Computer Science, pp. 1–6 (2013)
Skoutas, D., Sacharidis, D., Simitsis, A., et al.: Ranking and clustering web services using multicriteria dominance relationships. IEEE Trans. Serv. Comput. 3(3), 163–177 (2010)
Zhang, J., Xu, L., Li, Y.: Classifying Python code comments based on supervised learning. In: Meng, X., Li, R., Wang, K., Niu, B., Wang, X., Zhao, G. (eds.) WISA 2018. LNCS, vol. 11242, pp. 39–47. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-02934-0_4
Acknowledgment
The work is supported by National Key R&D Program of China (2018YFB1003901), National Natural Science Foundation of China (61832009, 61728203).
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Zhao, H., Chen, J., Xu, L. (2019). Semantic Web Service Discovery Based on LDA Clustering. In: Ni, W., Wang, X., Song, W., Li, Y. (eds) Web Information Systems and Applications. WISA 2019. Lecture Notes in Computer Science(), vol 11817. Springer, Cham. https://doi.org/10.1007/978-3-030-30952-7_25
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DOI: https://doi.org/10.1007/978-3-030-30952-7_25
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