TIAN Gang, WANG Jian, HE Keqing, et al., “Leveraging Auxiliary Knowledge for Web Service Clustering,” Chinese Journal of Electronics, vol. 25, no. 5, pp. 858-865, 2016, doi: 10.1049/cje.2016.06.008
Citation: TIAN Gang, WANG Jian, HE Keqing, et al., “Leveraging Auxiliary Knowledge for Web Service Clustering,” Chinese Journal of Electronics, vol. 25, no. 5, pp. 858-865, 2016, doi: 10.1049/cje.2016.06.008

Leveraging Auxiliary Knowledge for Web Service Clustering

doi: 10.1049/cje.2016.06.008
Funds:  This work is supported by the National Basic Research Program of China (973 Program) (No.2014CB340404), the National Natural Science Foundation of China (No.61202031, No.61373037), and the State Key Laboratory of Software Engineering Foundation (No.SKLSE 2014-10-07).
More Information
  • Corresponding author: WANG Jian (corresponding author) was born in 1980 and is a lecturer atWuhan University. He is a member of China Computer Federation (CCF). His current research interests include requirement engineering and service computing. (Email:jianwang@whu.edu.cn)
  • Received Date: 2014-07-22
  • Rev Recd Date: 2014-09-23
  • Publish Date: 2016-09-10
  • By grouping Web services that share similar functionalities, Web service clustering can greatly enhance Web service discovery and selection. Most existing clustering techniques are designed to handle long text documents. However, the descriptions of most publicly available Web services are in the form of short text, which impairs the quality of service clustering due to the sparseness of useful information. Towards this issue, we propose a new service clustering approach based on transfer learning from auxiliary long text data obtained from Wikipedia. To handle the inconsistencies in semantics and topics between service descriptions and auxiliary data, we introduce a novel topic model-Tag aided dual Author topical model (TD-ATM), which jointly learns two sets of topics on the two data sets and automatically couples the topic parameters to avoid the potential inconsistencies between these two data sets. Experimental results show the proposed approach outperforms several existing Web service clustering approaches.
  • loading
  • Q. Yu, X. Liu, A. Bouguettaya, et al., "Deploying and managing web services:Issues, solutions, and directions", The VLDB Journal, Vol.17, No.3, pp.537-572, 2008.
    K. Elgazzar, A.E. Hassan and P. Martin, "Clustering wsdl documents to bootstrap the discovery of web services", International Conference on Web Services, Los Angeles, USA, pp.147-154, 2009.
    Q. Yu, "Place semantics into context:Service community discovery from the wsdl corpus", Proc. of International Conference on Service Oriented Computing, Paphos, Cyprus, pp.188-203, 2011.
    Q. Yu and M. Rege, "On service community learning:A coclustering approach", Proc. of International Conference on Web Services., pp.283-290,2010.
    O. Jin, N. Liu, K. Zhao, et al., "Transferring topical knowledge from auxiliary long texts for short text clustering", Proc of the 20th ACM International Conference on Information and Knowledge Management, Glasgow, Scotland, UK, pp.775-784, 2011.
    M. Rosen-Zvi, T. Grihs, M. Steyvers, et al., "The author-topic model for authors and documents", Proc. of 20th Conference on Uncertainty in Articial Intelligence, Banff, Canada, pp.487-494, 2004.
    M. Rosen-Zvi, C. Chemudugunta, T. Grihs, et al., "Learning author-topic models from text corpora", ACM Transactions on Information Systems, Vol.28, No.1, pp.1-38, 2010.
    L. Chen, Q. Yu, Z. Zheng, et al., "WT-LDA:User tagging augmented LDA for web service clustering", Proc. of International Conference on Service Oriented Computing, Berlin, Germany, pp.162-176, 2013.
    C. Pop, B. Chifu and R. Salomie, "Semantic web service clustering for efficient discovery using an ant-based method", Intelligent Distributed Computing IV, Springer Berlin Heidelberg, Vol.315, No.1, pp.23-33, 2010.
    G. Cassar, P. Barnaghi and K. Moessner, "Probabilistic methods for service clustering", Proc. of the 4th International Workshop on Semantic Web Service Matchmaking and Resource Retrieval, Shanghai, China, pp.4-20, 2010.
    S. Dasgupta, S. Bhat and Y. Lee, "Taxonomic clustering of web service for efficient discovery", Proc. of International conference on Information and knowledge management, Toronto, Canada, pp.1617-1620,2010.
    P. Sun and C. Jiang, "Using service clustering to facilitate process-oriented semantic web service discovery", Chinese Journal of Computers, Vol.31, No.8, pp.1340-1353, 2008.
    W. Liu and W.Wong, "Web service clustering using text mining techniques", International Journal of Agent Oriented Software Engineering, Vol.3, No.1, pp.6-26, 2009.
    J. Wu, L. Chen, Z. Zheng, et al., "Clustering web services to facilitate service discovery", International Journal of Knowledge and Information Systems, Vol.38, No.1, pp.207-229, 2012.
    X. Dong, A. Halevy, J. Madhavan, et al., "Similarity Search for Web Services", Proc. of the 30th VLDB Conference, Toronto, Canada, pp.372-383, 2004.
    X. Hu, N. Sun, C. Zhang, et al., "Exploiting internal and external semantics for the clustering of short texts using world knowledge", Proc of the 18th ACM International Conference on Information and Knowledge Management, Hong Kong, China, pp.919-928, 2009.
    X. H. Phan, C. T. Nguyen, D. T. Le, et al., "A hidden topicbased framework toward building applications with short Web documents", IEEE Transactions on Knowledge and Data Engineering, Vol.23, No.7, pp.961-976, 2011.
    Z. Yang, K. Fan, Y. Lai, et al., "Short texts classification through reference document expansion", Chinese Journal of Electronics, Vol.23, No.2, pp.315-321, 2014.
    J. Tang, X. Wang and H. Gao, "Enriching short text representation in microblog for clustering", Frontiers of Computer Science, Vol.6, No.1, pp.88-101, 2012.
    C. Platzer, F. Rosenberg and S.Dustdar, "Web service clustering using multidimensional angles as proximity measures", ACM Trans. on Internet Technology, Vol.9, No.3, pp.1-26, 2009.
  • 加载中


    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (531) PDF downloads(691) Cited by()
    Proportional views


    DownLoad:  Full-Size Img  PowerPoint