Please wait a minute...
New Technology of Library and Information Service  2016, Vol. 32 Issue (5): 30-37    DOI: 10.11925/infotech.1003-3513.2016.05.04
Orginal Article Current Issue | Archive | Adv Search |
Clustering and Discovering Web Services with Topic Model
Li Hui,Hu Yunfeng()
School of Economics and Management, Xidian University, Xi’an 710071, China
Download:
Export: BibTeX | EndNote (RIS)      
Abstract  

[Objective] We propose an effective method to cluster and discover the needed Web services. [Methods] First, we employed the Biterm Topic Model to learn the latent topics of the Web service description corpus. Second, we retrieved and clustered each document’s topic distribution. Finally, we created a mechanism to discover Web service quickly. [Results] The proposed method achieved better precision rate and normalized discounted cumulative gain than methods using Latent Dirichlet Allocation and external corpus. [Limitations] Only considered functions of the Web services, and did not include the quality factors to the algorithm. [Conclusions] The proposed method could identify the needed services more accurately.
Key words： Web service      Topic model      Clustering      Discovery     
Received: 22 December 2015      Published: 24 June 2016
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
Li Hui
Hu Yunfeng

Cite this article:

Li Hui,Hu Yunfeng. Clustering and Discovering Web Services with Topic Model. New Technology of Library and Information Service, 2016, 32(5): 30-37.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2016.05.04     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2016/V32/I5/30

[1] Farrag T A, Saleh A I, Ali H A.Semantic Web Services Matchmaking: Semantic Distance-based Approach[J]. Computer and Electrical Engineering, 2013, 39(2): 497-511.
[2] Lu G, Wang T, Zhang G, et al.Semantic Web Services Discovery Based on Domain Ontology [C]. In: Proceedings of the 2012 World Automation Congress (WAC). 2012: 1-4.
[3] 石敏, 赵文栋, 张磊. 一种基于本体划分的语义Web服务发现算法[J]. 计算机工程, 2014, 40(2): 175-179.
[3] (Shi Min, Zhao Wendong, Zhang Lei.A Semantic Web Service Discovery Algorithm Based on Ontology Partition[J]. Computer Engineering, 2014, 40(2): 175-179.)
[4] Atkinson C, Bostan P, Hummel O, et al.A Practical Approach to Web Service Discovery and Retrieval[C]. In: Proceedings of the 2007 IEEE International Conference on Web Service. 2007: 241-248.
[5] Yan X, Guo J, Lan Y, et al.A Biterm Topic Model for Short Texts [C]. In: Proceedings of the 22nd International World Wide Web Conferences. 2013: 1445-1456.
[6] Qu M, Liu S, Bao T.On the Trusted Ontology Model for Evaluating the Semantic Web Services[C]. In: Proceedings of the 14th International Conference on Computer Supported Cooperative Work in Design.2010: 368-369.
[7] Kopecky J, Vitvar T, Bournez C, et al.Semantic Annotations for WSDL and XML Schema[J]. IEEE Internet Computing, 2007, 11(6): 60-67.
[8] 杨惠荣, 刘珊珊, 尹宝才, 等. 基于语义距离的 Web 服务匹配算法[J]. 北京工业大学学报, 2011, 37(4): 591-595.
[8] (Yang Huirong, Liu Shanshan, Yin Baocai, et al.Matching Algorithm of Services Based on Semantic Distance[J]. Journal of Beijing University of Technology, 2011, 37(4): 591-595.)
[9] Abramowicz W, Haniewicz K, Kaczmarek M, et al.Architecture for Web Services Filtering and Clustering [C]. In: Proceedings of the 2nd International Conference on Internet and Web Applications and Services.2007.
[10] Nayak R, Lee B.Web Service Discovery with Additional Semantics and Clustering [C]. In: Proceedings of the 2007 IEEE/WIC/ACM International Conference on Web Intelligence. 2007: 555-558.
[11] Cassar G, Barnaghi P, Moessner K.Probabilistic Methods for Service Clustering [J]. In: Proceeding of the 4th International Workshop on Service Matchmaking & Resource Retrieval. 2010.
[12] Blei D M, Ng A Y, Jordan M I.Latent DirichletAllocation[J]. Journal of Machine Learning Research, 2003, 3: 993-1022.
[13] Aznag M, Quafafou M, Rochd E M, et al.Probabilistic Topic Models for Web Services Clustering and Discovery[A]. // Service-Oriented and Cloud Computing[M]. Springer-Verlag Berlin Heidelberg, 2013.
[14] Blei D M, Lafferty J D.Correlated Topic Models[C]. In: Proceedings of the 23rd International Conference on Machine Learning. 2005.
[15] 魏强, 金芝, 许焱. 基于概率主题模型的物联网服务发现[J]. 软件学报, 2014, 25(8): 1640-1658.
[15] (Wei Qiang, Jin Zhi, Xu Yan.Service Discovery for Internet of Things Based on Probabilistic Topic Model[J]. Journal of Software, 2014, 25(8): 1640-1658.)
[16] Zhu Y, Li L, Luo L.Learning to Classify Short Text with Topic Model and External Knowledge[A]. //Knowledge Science, Engineering and Management[M]. Springer Berlin Heidelberg, 2013.
[17] Duda R O, Hart P E, Stork D G.模式分类[M]. 李宏东, 姚天翔等译. 第2版. 机械工业出版社, 2003.
[17] (Duda R O, Hart P E, Stork DG.Pattern Classification [M]. Translated by Li Hongdong, Yao Tianxiang, et al. The 2nd Edition. China Machine Press, 2003.)
[18] Lin J.Divergence Measures Based on the Shannon Entropy[J]. IEEE Transactions on Information Theory, 1991, 37(1): 145-151.
[19] Zhang Y L, Zheng Z B, Lyu M R.A QoS-aware Search Engine for Web Services [C]. In: Proceedings of the 8th International Conference on Web Services. Miami, Florida, USA. 2010.
[20] Cover T M, Hart P E.Nearest Neighbor Pattern Classification[J]. IEEE Transactions on Information Theory, 1967, 13(1): 21-27.
[1] Wang Ruolin, Niu Zhendong, Lin Qika, Zhu Yifan, Qiu Ping, Lu Hao, Liu Donglei. Disambiguating Author Names with Embedding Heterogeneous Information and Attentive RNN Clustering Parameters[J]. 数据分析与知识发现, 2021, 5(8): 13-24.
[2] Wang Xiwei,Jia Ruonan,Wei Yanan,Zhang Liu. Clustering User Groups of Public Opinion Events from Multi-dimensional Social Network[J]. 数据分析与知识发现, 2021, 5(6): 25-35.
[3] Lu Linong,Zhu Zhongming,Zhang Wangqiang,Wang Xiaochun. Cross-database Knowledge Integration and Fingerprint of Institutional Repositories with Lingo3G Clustering Algorithm[J]. 数据分析与知识发现, 2021, 5(5): 127-132.
[4] Yi Huifang,Liu Xiwen. Analyzing Patent Technology Topics with IPC Context-Enhanced Context-LDA Model[J]. 数据分析与知识发现, 2021, 5(4): 25-36.
[5] Dai Bing,Hu Zhengyin. Review of Studies on Literature-Based Discovery[J]. 数据分析与知识发现, 2021, 5(4): 1-12.
[6] Zhang Xin,Wen Yi,Xu Haiyun. A Prediction Model with Network Representation Learning and Topic Model for Author Collaboration[J]. 数据分析与知识发现, 2021, 5(3): 88-100.
[7] Zhang Mengyao, Zhu Guangli, Zhang Shunxiang, Zhang Biao. Grouping Microblog Users of Trending Topics Based on Sentiment Analysis[J]. 数据分析与知识发现, 2021, 5(2): 43-49.
[8] Zhao Tianzi, Duan Liang, Yue Kun, Qiao Shaojie, Ma Zijuan. Generating News Clues with Biterm Topic Model[J]. 数据分析与知识发现, 2021, 5(2): 1-13.
[9] Ding Hao, Ai Wenhua, Hu Guangwei, Li Shuqing, Suo Wei. A Personalized Recommendation Model with Time Series Fluctuation of User Interest[J]. 数据分析与知识发现, 2021, 5(11): 45-58.
[10] Wang Song, Yang Yang, Liu Xinmin. Discovering Potentialities of User Ideas from Open Innovation Communities with Graph Attention Network[J]. 数据分析与知识发现, 2021, 5(11): 89-101.
[11] Chen Hao, Zhang Mengyi, Cheng Xiufeng. Identifying Cross-Region Patent Collaboration Opportunities Using LDA and Decision Trees——Case Study of Universities from Guangdong and Wuhan[J]. 数据分析与知识发现, 2021, 5(10): 37-50.
[12] Yang Chen, Chen Xiaohong, Wang Chuhan, Liu Tingting. Recommendation Strategy Based on Users’ Preferences for Fine-Grained Attributes[J]. 数据分析与知识发现, 2021, 5(10): 94-102.
[13] Yu Fengchang,Cheng Qikai,Lu Wei. Locating Academic Literature Figures and Tables with Geometric Object Clustering[J]. 数据分析与知识发现, 2021, 5(1): 140-149.
[14] Wu Jinming,Hou Yuefang,Cui Lei. Automatic Expression of Co-occurrence Clustering Based on Indexing Rules of Medical Subject Headings[J]. 数据分析与知识发现, 2020, 4(9): 133-144.
[15] Shao Qi,Mu Dongmei,Wang Ping,Jin Chunyan. Identifying Subjects of Online Opinion from Public Health Emergencies[J]. 数据分析与知识发现, 2020, 4(9): 68-80.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626，82624938   E-mail:jishu@mail.las.ac.cn