May  2018, 1(2): 101-119. doi: 10.3934/mfc.2018006

Web services recommendation leveraging semantic similarity computing

1. 

School of Information Engineering, China University of Geosciences (Beijing), China

2. 

Computer Science Department, University of California, Santa Cruz, USA

3. 

School of Information Engineering, China University of Geosciences (Beijing), China

4. 

Computer Science Department, TELECOM SudParis, France

* Corresponding author: Zhangbing Zhou

Received  December 2017 Revised  February 2018 Published  May 2018

Fund Project: 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.

Citation: Boran Hu, Zehui Cheng, Zhangbing Zhou. Web services recommendation leveraging semantic similarity computing. Mathematical Foundations of Computing, 2018, 1 (2) : 101-119. doi: 10.3934/mfc.2018006
References:
[1]

P. BrazierA. ChebotkoE. GonzalezA. Kashlev and A. Piazza, Supporting Geosciences Web Services Metadata Management and Discovery, IEEE International Conference on Services Computing, (2010), 625-626.   Google Scholar

[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.  Google Scholar

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B. ChengS. ZhaoC. 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.   Google Scholar

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Z. ChengB. YaoX. Wang and Z. Zhou, Web service sub-chain recommendation leveraging graph searching, IEEE Computers, Communications and IT Applications Conference, (2014), 271-275.   Google Scholar

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S. ChengZ. CaiJ. Li and Xiaolin Fang, Drawing dominant dataset from big sensory data in wireless sensor networks, INFOCOM, (2015), 531-539.   Google Scholar

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S. ChengZ. Cai and Jianzhong Li, Curve Query Processing in Wireless Sensor Networks, IEEE Transactions on Vehicular Technology, 64 (2015), 5198-5209.   Google Scholar

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S. ChengZ. CaiJ. 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.   Google Scholar

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U. ChukmolA. N. Benharkat and Y. Amghar, Towards a user-oriented framework for web service discovery, reuse and evolution, IEEE Computer Society, (2009), 415-422.   Google Scholar

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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.   Google Scholar

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D. FitznerJ. Hoffmann and E. Klien, Functional description of geoprocessing services as conjunctive datalog queries, Geoinformatica, 15 (2011), 191-221.   Google Scholar

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Y. GaoJ. NaB. ZhangL. Yang and Q. Gong, Optimal web services selection using dynamic programming, IEEE Symposium on Computers and Communications, (2006), 365-370.   Google Scholar

[12]

E. KhanfirR. 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.   Google Scholar

[13]

X. LiL. DiW. HanP. Zhao and U. Dadi, Sharing geoscience algorithms in a Web service-oriented environment (GRASS GIS example), Computer Geosciences, 36 (2010), 1060-1068.   Google Scholar

[14]

S. LiJ. WenF. LuoM. GaoJ. 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.   Google Scholar

[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.   Google Scholar

[16]

W. LiC. YangD. NebertR. RaskinP. HouserH. Wu and Z. Li, Semantic-based web service discovery and chaining for building an Arctic spatial data infrastructure, Computers & Geosciences, 37 (2011), 1752-1762.   Google Scholar

[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.   Google Scholar

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G. LiuY. ZhaoZ. 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.   Google Scholar

[19]

X. LuoH. 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.   Google Scholar

[20]

H. MaA. 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.   Google Scholar

[21]

L. MiaoY. ZhouW. 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.   Google Scholar

[22]

A. MohanM. Ebrahimi and S. Lu, A folksonomy-based social recommendation system for scientific workflow reuse, IEEE International Conference on Services Computing, (2015), 704-711.   Google Scholar

[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.   Google Scholar

[24]

L. PurohitS. 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.  Google Scholar

[25]

J. StarlingerS. CohenboulakiaS. KhannaS. 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.  Google Scholar

[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.   Google Scholar

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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.  Google Scholar

[28]

Y. WangG. YinZ. CaiY. 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.  Google Scholar

[29]

Y. WangH. Li and A. Luo, A hybrid classification matching method for geospatial services, Transactions in Gis, 16 (2012), 781-805.   Google Scholar

[30]

J. WangP. GaoY. MaK. 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.  Google Scholar

[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.  Google Scholar

[32]

M. YokoyamaY. 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.  Google Scholar

[33]

L. YuJ. ZhouJ. ZhangF. Wei and J. Wang, Time-aware semantic web service recommendation, 2015 IEEE International Conference on Services Computing, (2015), 664-671.   Google Scholar

[34]

P. YueJ. GongL. DiL. 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.  Google Scholar

[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. Google Scholar

[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.  Google Scholar

[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.  Google Scholar

[38]

Z. ZhouZ. ChengL.-J. ZhangW. 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.  Google Scholar

[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.  Google Scholar

[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.  Google Scholar

show all references

References:
[1]

P. BrazierA. ChebotkoE. GonzalezA. Kashlev and A. Piazza, Supporting Geosciences Web Services Metadata Management and Discovery, IEEE International Conference on Services Computing, (2010), 625-626.   Google Scholar

[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.  Google Scholar

[3]

B. ChengS. ZhaoC. 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.   Google Scholar

[4]

Z. ChengB. YaoX. Wang and Z. Zhou, Web service sub-chain recommendation leveraging graph searching, IEEE Computers, Communications and IT Applications Conference, (2014), 271-275.   Google Scholar

[5]

S. ChengZ. CaiJ. Li and Xiaolin Fang, Drawing dominant dataset from big sensory data in wireless sensor networks, INFOCOM, (2015), 531-539.   Google Scholar

[6]

S. ChengZ. Cai and Jianzhong Li, Curve Query Processing in Wireless Sensor Networks, IEEE Transactions on Vehicular Technology, 64 (2015), 5198-5209.   Google Scholar

[7]

S. ChengZ. CaiJ. 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.   Google Scholar

[8]

U. ChukmolA. N. Benharkat and Y. Amghar, Towards a user-oriented framework for web service discovery, reuse and evolution, IEEE Computer Society, (2009), 415-422.   Google Scholar

[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.   Google Scholar

[10]

D. FitznerJ. Hoffmann and E. Klien, Functional description of geoprocessing services as conjunctive datalog queries, Geoinformatica, 15 (2011), 191-221.   Google Scholar

[11]

Y. GaoJ. NaB. ZhangL. Yang and Q. Gong, Optimal web services selection using dynamic programming, IEEE Symposium on Computers and Communications, (2006), 365-370.   Google Scholar

[12]

E. KhanfirR. 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.   Google Scholar

[13]

X. LiL. DiW. HanP. Zhao and U. Dadi, Sharing geoscience algorithms in a Web service-oriented environment (GRASS GIS example), Computer Geosciences, 36 (2010), 1060-1068.   Google Scholar

[14]

S. LiJ. WenF. LuoM. GaoJ. 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.   Google Scholar

[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.   Google Scholar

[16]

W. LiC. YangD. NebertR. RaskinP. HouserH. Wu and Z. Li, Semantic-based web service discovery and chaining for building an Arctic spatial data infrastructure, Computers & Geosciences, 37 (2011), 1752-1762.   Google Scholar

[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.   Google Scholar

[18]

G. LiuY. ZhaoZ. 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.   Google Scholar

[19]

X. LuoH. 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.   Google Scholar

[20]

H. MaA. 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.   Google Scholar

[21]

L. MiaoY. ZhouW. 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.   Google Scholar

[22]

A. MohanM. Ebrahimi and S. Lu, A folksonomy-based social recommendation system for scientific workflow reuse, IEEE International Conference on Services Computing, (2015), 704-711.   Google Scholar

[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.   Google Scholar

[24]

L. PurohitS. 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.  Google Scholar

[25]

J. StarlingerS. CohenboulakiaS. KhannaS. 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.  Google Scholar

[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.   Google Scholar

[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.  Google Scholar

[28]

Y. WangG. YinZ. CaiY. 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.  Google Scholar

[29]

Y. WangH. Li and A. Luo, A hybrid classification matching method for geospatial services, Transactions in Gis, 16 (2012), 781-805.   Google Scholar

[30]

J. WangP. GaoY. MaK. 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.  Google Scholar

[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.  Google Scholar

[32]

M. YokoyamaY. 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.  Google Scholar

[33]

L. YuJ. ZhouJ. ZhangF. Wei and J. Wang, Time-aware semantic web service recommendation, 2015 IEEE International Conference on Services Computing, (2015), 664-671.   Google Scholar

[34]

P. YueJ. GongL. DiL. 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.  Google Scholar

[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. Google Scholar

[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.  Google Scholar

[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.  Google Scholar

[38]

Z. ZhouZ. ChengL.-J. ZhangW. 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.  Google Scholar

[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.  Google Scholar

[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.  Google Scholar

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
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