Abstract
The classification of Web services with high similarity is conducive to the promotion for service management and service discovery. With the increasing number of Web services, how to accurately and efficiently classify the Web services becomes an urgent and challenging task. Although the existing methods achieve significant results in the task for service classification via integrating the structure information of service network with the content features of service node, it fails to discriminate the importance of neighbor services in the service network on the service node needed to be classified. To solve this problem, we propose a Web services classification method based on graph attention network. Firstly, according to the composition and shared annotation relationship of Web services, it applies the description documents, tags of Web services and the call relationship between mashups and services to build a service relationship network. Then, the attention coefficient of service nodes in the network is calculated by the self-attention mechanism, and different service nodes in the neighborhood are assigned different weights to classify Web services. Through the graph attention network, the content features of Web service can be well integrated with its structure information. Also, the learned attention weight is more interpretable. The experimental results on the real dataset of ProgrammableWeb platform show that the precision, recall and macro-F1 of the proposed method are greatly improved compared to those of GCN, Node2vec, DeepWalk and Line.
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Zhou, Z., Sellami, M., Gaaloul, W., Barhamgi, M., Defude, B.: Data providing services clustering and management for facilitating service discovery and replacement. IEEE Trans. Autom. Sci. Eng. 10(4), 1131–1146 (2013)
Skoutas, D., Sacharidis, D., Simitsis, A., Sellis, T.: Ranking and clustering web services using multicriteria dominance relationships. IEEE Trans. Serv. Comput. 3(3), 163–177 (2010)
Zhang, M., Liu, X., Zhang, R., Sun, H.: A web service recommendation approach based on QoS prediction using fuzzy clustering. In: 2012 IEEE Ninth International Conference on Services Computing, pp. 138–145 (2012)
Wang, H., Yang, Z., Yu, Q.: Online reliability prediction via long short term memory for service-oriented systems. In: 2017 IEEE International Conference on Web Services, pp. 81–88 (2017)
Xia, B., Fan, Y., Tan, W., Huang, K., Zhang, J., Wu, C.: Category-aware API clustering and distributed recommendation for automatic mashup creation. IEEE Trans. Serv. Comput. 8(5), 674–687 (2015)
Chen, L., et al.: WTCluster: utilizing tags for web services clustering. In: Kappel, G., Maamar, Z., Motahari-Nezhad, H.R. (eds.) ICSOC 2011. LNCS, vol. 7084, pp. 204–218. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25535-9_14
Min, S.H.I., Jian-Xun, L.I.U., Dong, Z., Bu-Qing, C.A.O., Yi-Ping, W.E.N.: Multi-relational topic model-based approach for web services clustering. Chin. J. Comput. 42(4), 820–836 (2019)
Chen, L., Wang, Y., Yu, Q., Zheng, Z., Wu, J.: WT-LDA: user tagging augmented LDA for web service clustering. In: Basu, S., Pautasso, C., Zhang, L., Fu, X. (eds.) ICSOC 2013. LNCS, vol. 8274, pp. 162–176. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-45005-1_12
Shi, M., Liu, J., Zhou, D., Tang, M., Cao, B.: WE-LDA: a word embeddings augmented LDA model for web services clustering. In: 2017 IEEE International Conference on Web Services, pp. 9–16 (2017)
Cao, B., et al.: Mashup service clustering based on an integration of service content and network via exploiting a two-level topic model. In: 2016 IEEE International Conference on Web Services, pp. 212–219 (2016)
Cao, B., Liu, X.F., Rahman, M.M., Li, B., Liu, J., Tang, M.: Integrated content and network-based service clustering and web APIs recommendation for mashup development. IEEE Trans. Serv. Comput. 13(1), 99–113 (2020)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: 2017 International Conference on Learning Representations (2017)
Peng, C., Sun, Z., Bing, L., Wei, Y.: Recurrent attention network on memory for aspect sentiment analysis. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 452–461 (2017)
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)
Li, H., Liu, J., Cao, B., Tang, M., Liu, X., Li, B.: Integrating tag, topic, co-occurrence, and popularity to recommend web APIs for mashup creation. In: 2017 IEEE International Conference on Services Computing, pp. 84–91 (2017)
Shi, M., Liu, J., Zhou, D., Tang, M., Xie, F., Zhang, T.: A probabilistic topic model for mashup tag recommendation. In: 2016 IEEE International Conference on Web Services, pp. 444–451 (2016)
Xiong, W., Wu, Z., Li, B., Gu, Q.: A Learning approach to QoS prediction via multi-dimensional context. In: 2017 IEEE International Conference on Web Services, pp. 164–171 (2017)
Zhang, Y., Zheng, Z., Lyu, M.R.: WSPred: a time-aware personalized QoS prediction framework for web services. In: 2011 IEEE 22nd International Symposium on Software Reliability Engineering, pp. 210–219 (2011)
Kang, G., Liu, J., Tang, M., Liu, X., Cao, B., Xu, Y.: AWSR: active web service recommendation based on usage history. In: 2012 IEEE 19th International Conference on Web Services, pp. 186–193 (2012)
Elgazzar, K., Hassan, A.E., Martin, P.: Clustering WSDL documents to bootstrap the discovery of web services. In: 2010 IEEE International Conference on Web Services, pp. 147–154 (2010)
Wu, J., Chen, L., Zheng, Z., Lyu, M.R., Wu, Z.: Clustering web services to facilitate service discovery. Knowl. Inf. Syst. 38(1), 207–229 (2013). https://doi.org/10.1007/s10115-013-0623-0
Cao, Y., Liu, J., Cao, B., Shi, M., Wen, Y., Peng, Z.: Web services classification with topical attention based Bi-LSTM. In: Wang, X., Gao, H., Iqbal, M., Min, G. (eds.) CollaborateCom 2019. LNICST, vol. 292, pp. 394–407. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30146-0_27
Yao, L., Mao, C., Luo, Y.: Graph convolutional networks for text classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 7370–7377 (2019)
Shi, M., Liu, J., Cao, B., Wen, Y., Zhang, X.: A prior knowledge based approach to improving accuracy of web services clustering. In: 2018 IEEE International Conference on Services Computing, pp. 1–8 (2018)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: 2015 International Conference on Learning Representations (2015)
Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710. Association for Computing Machinery, New York (2014)
Grover, A., Leskovec, J.: Node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864. Association for Computing Machinery, New York (2016)
Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: LINE: large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1067–1077. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE (2015)
Acknowledgement
Our work is supported by the National Natural Science Foundation of China (No. 61873316, 61872139, 61832014 and 61702181), the National Key R&D Program of China (2018YFB1402800, 2017YFB1400602), and the Natural Science Foundation of Hunan Province (No. 2018JJ3190, 2018JJ2136). Buqing Cao is the corresponding author of this paper.
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Peng, M., Cao, B., Chen, J., Liu, J., Li, B. (2021). SC-GAT: Web Services Classification Based on Graph Attention Network. In: Gao, H., Wang, X., Iqbal, M., Yin, Y., Yin, J., Gu, N. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 349. Springer, Cham. https://doi.org/10.1007/978-3-030-67537-0_31
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