Article Contents
Article Contents

Web services recommendation leveraging semantic similarity computing

• * Corresponding author: Zhangbing Zhou
The first author is supported by the National Natural Science Foundation of China (No. 61379126 and 61662021).
• With the popularity of Web services adopted for supporting domain applications, recommending and composing appropriate services with respect to user requirements is a challenge. This paper proposes a dynamic programming and variable length genetic algorithm for the recommendation and composition of Web services. Generally, starting and ending services are determined leveraging the constructed service network model. Based on which, services are selected and composed, such that these services should be more appropriate on satisfying users' requirements. Experimental evaluation result shows that our technique is effective and can improve the accuracy of service recommendation.

Mathematics Subject Classification: Primary: 58F15, 58F17; Secondary: 53C35.

 Citation:

• Figure 1.  The process of similarity computing. The similarity calculation is mainly based on some semantic computing methods

Figure 2.  The simplified web service network model based on spatial Web services in CSISS OWSs, which consists of 6 operations and 36 directed edges

Figure 3.  State change of Dynamic Programming, the state will change until all the operations are included, and only part of the steps are shown in the diagram

Figure 4.  The computation process of cross operator.In order to simplify, this graph is the first calculation process, the subsequent variable length operator will make the length of the chromosome change, the length will become uncertain

Figure 5.  The process of decreasing the length in variable-length operator. We decrease the length of subsegments by deleting some non-critical operations randomly

Figure 6.  The process of increasing the length in variable-length operator. We increase the length of subsegments by adding some non-critical operations randomly

Figure 7.  The run time and the aim function of DP algorithm when the length of paths is set from 3 to 9

Figure 8.  The average weight of DP algorithm when the length of paths is set from 3 to 9

Figure 9.  Comparison of run time for DP and GA, when the number of operations is set from 30 to 70

Figure 10.  Comparison of aim function for DP and GA, when the number of operations is set from 30 to 70

Figure 11.  Comparison of average weight for DP and GA, when the number of operations is set from 30 to 70

•  [1] P. Brazier, A. Chebotko, E. Gonzalez, A. Kashlev and A. Piazza, Supporting Geosciences Web Services Metadata Management and Discovery, IEEE International Conference on Services Computing, (2010), 625-626. [2] Z. Cai, Z. He, X. Guan and Y. Li, Collective data-sanitization for preventing sensitive information inference attacks in social networks, EEE Transactions on Dependable and Secure Computing, (2016). doi: 10.1109/TDSC.2016.2613521. [3] B. Cheng, S. Zhao, C. Li and J. Chen, A web services discovery approach based on mining underlying interface semantics, IEEE Transactions on Knowledge and Data Engineering, 29 (2017), 950-962. [4] Z. Cheng, B. Yao, X. Wang and Z. Zhou, Web service sub-chain recommendation leveraging graph searching, IEEE Computers, Communications and IT Applications Conference, (2014), 271-275. [5] S. Cheng, Z. Cai, J. Li and Xiaolin Fang, Drawing dominant dataset from big sensory data in wireless sensor networks, INFOCOM, (2015), 531-539. [6] S. Cheng, Z. Cai and Jianzhong Li, Curve Query Processing in Wireless Sensor Networks, IEEE Transactions on Vehicular Technology, 64 (2015), 5198-5209. [7] S. Cheng, Z. Cai, J. Li and Hong Gao, Extracting Kernel Dataset from Big Sensory Data in Wireless Sensor Networks, IEEE Transactions on Knowledge and Data Engineering, 29 (2017), 813-827. [8] U. Chukmol, A. N. Benharkat and Y. Amghar, Towards a user-oriented framework for web service discovery, reuse and evolution, IEEE Computer Society, (2009), 415-422. [9] F. Dafedar and K. F. Bharati, A fast collaborative filtering approach for web personalized recommendation system, 2017 International Conference on Information Communication and Embedded Systems (ICICES), (2017), 1-7. [10] D. Fitzner, J. Hoffmann and E. Klien, Functional description of geoprocessing services as conjunctive datalog queries, Geoinformatica, 15 (2011), 191-221. [11] Y. Gao, J. Na, B. Zhang, L. Yang and Q. Gong, Optimal web services selection using dynamic programming, IEEE Symposium on Computers and Communications, (2006), 365-370. [12] E. Khanfir, R. B. Djmeaa and I. Amous, Self-adaptive goal-driven web service composition based on context and QoS, 2017 IEEE 14th International Conference on e-Business Engineering (ICEBE), (2017), 201-207. [13] X. Li, L. Di, W. Han, P. Zhao and U. Dadi, Sharing geoscience algorithms in a Web service-oriented environment (GRASS GIS example), Computer Geosciences, 36 (2010), 1060-1068. [14] S. Li, J. Wen, F. Luo, M. Gao, J. Zeng and Z. Dong, A New QoS-Aware Web Service Recommendation System Based on Contextual Feature Recognition at Server-Side, IEEE Transactions on Network and Service Management, 14 (2017), 332-342. [15] H. Li and B. Wu, Adaptive geo-information processing service evolution: Reuse and local modification method, Isprs Journal of Photogrammetry Remote Sensing, 83 (2013), 165-183. [16] W. Li, C. Yang, D. Nebert, R. Raskin, P. Houser, H. Wu and Z. Li, Semantic-based web service discovery and chaining for building an Arctic spatial data infrastructure, Computers & Geosciences, 37 (2011), 1752-1762. [17] S. Li and M. Chen, An adaptive-GA based QoS driven service selection for Web services composition, International Conference on Computer Application and System Modeling, (2010), 416-418. [18] G. Liu, Y. Zhao, Z. Wang and Y. Liu, A Service Chain Discovery and Recommendation Scheme Using Complex Network Theory, Mathematical Problems in Engineering, 2014, (2014-1-16), 2014 (2014), 1-6. [19] X. Luo, H. Luo and X. Chang, Online optimization of collaborative web service QoS prediction based on approximate dynamic programming, International Conference on Identification, Information and Knowledge in the Internet of Things, (2014), 80-83. [20] H. Ma, A. Wang and M. Zhang, A hybrid approach using genetic programming and greedy search for QoS-aware web service composition, Springer Berlin Heidelberg, (2015), 180-205. [21] L. Miao, Y. Zhou, W. Cheng and Jing Guo, 2015 Fourth international conference on agro-geoinformatics (agro-geoinformatics), 2015 Fourth International Conference on Agro-Geoinformatics (Agro-geoinformatics), (2015), 217-220. [22] A. Mohan, M. Ebrahimi and S. Lu, A folksonomy-based social recommendation system for scientific workflow reuse, IEEE International Conference on Services Computing, (2015), 704-711. [23] M. Nandhini and S, M. Sendil, Web service quality composition determination using genetic algorithm in sematic web, International Journal of Computer Science and Information Technologies, 3 (2013), 4704-4706. [24] L. Purohit, S. Kumar and D. Kshirsagar, Analyzing genetic algorithm for web service selection, International Conference on Next Generation Computing Technologies, (2016), 999-1003.  doi: 10.1109/NGCT.2015.7375271. [25] J. Starlinger, S. Cohenboulakia, S. Khanna, S. Davidson and U. Leser, Layer decomposition: An effective structure-based approach for scientific workflow similarity, American Physical Society, (2014), 314-323.  doi: 10.1109/eScience.2014.19. [26] D. M. Stewart and J Cody, A self-organizing P2P framework for collective service discovery, Journal of Network Computer Applications, 39 (2014), 214-222. [27] G. Vadivelou and E. Ilavarasan, QoS-based web service ranking model considering decision making methods, 2017 World Congress on Computing and Communication Technologies (WCCCT), (2017), 198-202.  doi: 10.1109/WCCCT.2016.56. [28] Y. Wang, G. Yin, Z. Cai, Y. Dong and H. Dong, A trust-based probabilistic recommendation model for social networks, Journal of Network and Computer Applications, 55 (2015), 59-67.  doi: 10.1016/j.jnca.2015.04.007. [29] Y. Wang, H. Li and A. Luo, A hybrid classification matching method for geospatial services, Transactions in Gis, 16 (2012), 781-805. [30] J. Wang, P. Gao, Y. Ma, K. He and P. C. K. Hung, A web service discovery approach based on common topic groups extraction, IEEE Transactions on Services Computing, 5 (2017), 10193-10208.  doi: 10.1109/ACCESS.2017.2712744. [31] H. Xia and T. Yoshida, Web service recommendation with ontology-based similarity measure, Second International Conference on Innovative Computing, Informatio and Control (ICICIC 2007), (2007), 412-412.  doi: 10.1109/ICICIC.2007.620. [32] M. Yokoyama, Y. Kiyoki and T. Mita, Similarity-ranking method based on semantic computing for a context-aware system, International Conference on Knowledge Creation and Intelligent Computing, (2017), 21-27.  doi: 10.1109/KCIC.2016.7883620. [33] L. Yu, J. Zhou, J. Zhang, F. Wei and J. Wang, Time-aware semantic web service recommendation, 2015 IEEE International Conference on Services Computing, (2015), 664-671. [34] P. Yue, J. Gong, L. Di, L. He and Y. Wei, Integrating semantic web technologies and geospatial catalog services for geospatial information discovery and processing in cyberinfrastructure, Geoinformatica, 15 (2011), 273-303.  doi: 10.1007/s10707-009-0096-1. [35] Z. Zhao, X. Hong and S. Wang, A web service composition method based on merging genetic algorithm and ant colony algorithm, IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, (2015), 1007–1011. [36] Z. Zhao, X. Hong and S. Wang, A web service composition method based on merging genetic algorithm and ant colony algorithm, IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, (2015), 1007–1011. doi: 10.1109/CIT/IUCC/DASC/PICOM.2015.152. [37] X. Zheng, Z. Cai, J. Li and H. Gao, Location-privacy-aware review publication mechanism for local business service systems, The 36th Annual IEEE International Conference on Computer Communications (INFOCOM 2017) (2017). doi: 10.1109/INFOCOM.2017.8056976. [38] Z. Zhou, Z. Cheng, L.-J. Zhang, W. Gaaloul and K. Ning, Scientific workflow clustering and recommendation leveraging layer hierarchical analysis, IEEE Transactions on Services Computing, 11 (2018), 169-183.  doi: 10.1109/TSC.2016.2542805. [39] X. Zhou and F. Mao, A semantics web service composition approach based on cloud computing, Fourth International Conference on Computational and Information Sciences, (2012), 807-810.  doi: 10.1109/ICCIS.2012.43. [40] Y. Zhu, A book recommendation algorithm based on collaborative filtering, 2016 5th International Conference on Computer Science and Network Technology (ICCSNT), (2016), 286-289.  doi: 10.1109/ICCSNT.2016.8070165.

Figures(11)