Cluster Computing

, Volume 21, Issue 1, pp 1009–1022 | Cite as

A collaboration centric approach for building the semantic knowledge network for knowledge advantage machine

  • Ming Qu
  • Yuchun ChangEmail author


Knowledge advantage machine (KaM) is an advanced system for knowledge exploitation. In this paper, we propose a Collaboration Centric Behavior Model, which helps one or more knowledge-workers to discover and link useful knowledge objects dubbed JANs into a semantic knowledge network. The JAN is constructed as a semantic web service to semantically present three categories of service behavior: expected service behavior that presents what requestor expects it to serve; actual service behavior that presents how it offers its service; and quality evaluation that presents whether its service behavior is consistent with requestor’s expectation by checking conformance. On the basis of the KaM architecture, we build a process model to implement to discovery a JAN and link different JANs as a personal knowledge network or a group knowledge network. This is illustrated using an academic research scenario. Experimental results show that the proposed method is feasible and effective.


Knowledge advantage machine Collaboration Centric Behavior Model Service behavior Semantic knowledge network Semantic web service 



This research was supported by National Natural Science Foundation of China (No. 61472160). This work was supported by the Development and Reform Commission of Jilin Province (No. 2015Y041).


  1. 1.
    Machado, A., Maran, V., Augustin, I., Wives, L.K., de Oliveira, J.P.M.: Reactive, proactive, and extensible situation-awareness in ambient assisted living. Expert Syst. Appl. 76(15), 21–35 (2017)CrossRefGoogle Scholar
  2. 2.
    Castillejo, E., Almeida, A., López-de-Ipiña, D., Chen, L.: Modeling users, context and devices for ambient assisted living environments. Sensors 14(3), 5354–5391 (2014)CrossRefGoogle Scholar
  3. 3.
    Forkan, A., Khalil, I., Tari, Z.: CoCaMAAL: a cloud-oriented context-aware middleware in ambient assisted living. Fut. Gener. Comput. Syst. 35, 114–127 (2014)CrossRefGoogle Scholar
  4. 4.
    Kaldeli, E., Lazovik, A., Aiello, M.: Domain-independent planning for services in uncertain and dynamic environments. Artif. Intell. 236, 30–64 (2016)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Abbasi, A.: A longitudinal analysis of link formation on collaboration networks. J. Inf. 10(3), 685–692 (2016)CrossRefGoogle Scholar
  6. 6.
    Kwon, O., Son, W.-S., Jung, W.-S.: The double power law in human collaboration behavior: the case of Wikipedia. Phys. A 461, 85–91 (2016)CrossRefGoogle Scholar
  7. 7.
    Arazy, O., Gellatly, I., Brainin, E., Nov, O.: Motivation to share knowledge using wiki technology and the moderating effect of role perceptions. J. Assoc. Inf. Sci. Technol. 67(10), 2362–2378 (2016)CrossRefGoogle Scholar
  8. 8.
    Rani, M., Nayak, R., Vyas, O.P.: An ontology-based adaptive personalized e-learning system, assisted by software agents on cloud storage [J]. Knowl. Based Syst. 90, 33–48 (2015)CrossRefGoogle Scholar
  9. 9.
    Jo, I., Jung, I.Y.: Smart learning of logo detection for mobile phone applications. Multimed. Tools Appl. 75(21), 13211–13233 (2016)CrossRefGoogle Scholar
  10. 10.
    Hamari, J., Sjoklint, M., Ukkonen, A.: The sharing economy: why people participate in collaborative consumption. J. Assoc. Inf. Sci. Technol. 67(9), 2047–2059 (2016)CrossRefGoogle Scholar
  11. 11.
    Mohaisen, M., Mohaisen, A.: Characterizing collaboration in social network-enabled routing. KSII Trans. Internet Inf. Syst. 10(4), 1643–1660 (2016)Google Scholar
  12. 12.
    Li, Q., Liu, S., Qu, M.: Modeling the web service behavior semantically based on the ontology. Acta Electron. Sin. 43(4), 601–604 (2015). (in Chinese)Google Scholar
  13. 13.
    Yahyaoui, H., Own, H.S., Malik, Z.: Modeling and classification of service behaviors. Expert Syst. Appl. 42(21), 7610–7619 (2015)CrossRefGoogle Scholar
  14. 14.
    Mitrevski, P.J., Hristoski, I.S.: Behavioral-based performability modeling and evaluation of e-commerce systems. Electron. Commer. Res. Appl. 13(5), 320–340 (2014)CrossRefGoogle Scholar
  15. 15.
    Criado, J., Rodriguez-Gracia, D., Iribarne, L., Padilla, N.: Toward the adaptation of component-based architectures by model transformation: behind smart user interfaces. Softw. Pract. Exp. 45(12), 1677–1718 (2015)CrossRefGoogle Scholar
  16. 16.
    Riccobene, E., Scandurra, P.: A formal framework for service modeling and prototyping. Form. Asp. Comput. 26(6), 1088–1113 (2014)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Getir, S., Challenger, M., Kardas, G.: The formal semantics of a domain-specific modeling language for semantic web enabled multi-agent systems. Int. J. Coop. Inf. Syst. 23, 145005 (2014). doi: 10.1142/S0218843014500051 CrossRefGoogle Scholar
  18. 18.
    Rajaram, K., Babu, C., Adiththan, A.: Dynamic transaction aware web service selection. Int. J. Coop. Inf. Syst. 23, 145004 (2014). doi: 10.1142/S021884301450004X CrossRefGoogle Scholar
  19. 19.
    Vairetti, C., Alarcon, R.: A Semantic approach for dynamically determining complex composed service behaviour. J. Web Eng. 14(3–4), 310–338 (2016)Google Scholar
  20. 20.
    Canton-Puerto, D.G., Moo-Mena, F., Uc-Cetina, V.: QoS-based web services selection using a hidden Markov model. J. Comput. 12(1), 48–56 (2017)Google Scholar
  21. 21.
    Mehdi, M., Bouguila, N., Bentahar, J.: Trust and reputation of web services through QoS correlation lens. IEEE Trans. Serv. Comput. 9(6), 968–981 (2016)CrossRefGoogle Scholar
  22. 22.
    Yu, L., Motani, M., Wong, W.-C.: A QoE-aware resource distribution framework incentivizing context sharing and moderate competition. IEEE-ACM Trans. Netw. 24(3), 1364–1377 (2016)CrossRefGoogle Scholar
  23. 23.
    Azimi, R., Ghayekhloo, M., Ghofrani, M., Sajedi, H.: A novel clustering algorithm based on data transformation approaches. Expert Syst. Appl. 76(15), 59–70 (2017)CrossRefGoogle Scholar
  24. 24.
    Malinen, M., Mariescu-Istodor, R., Fränti, P.: K-means*: clustering by gradual data transformation. Pattern Recognit. 47(10), 3376–3386 (2014)CrossRefGoogle Scholar
  25. 25.
    Evan, S., Jonathan, L., Trevor, D.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 29(4), 640–651 (2016)Google Scholar
  26. 26.
    Espi-Beltran, J.V., Gilart-Iglesias, V., Ruiz-Fernandez, D.: Enabling distributed manufacturing resources through SOA: the REST approach. Robot. Comput. Integr. Manuf. 46, 156–165 (2017)CrossRefGoogle Scholar
  27. 27.
    Liu, J., Xia, Z.: An approach of web service organization using Bayesian network learning. J. Web Eng. 16(3–4), 252–276 (2017)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyJilin UniversityChangchunChina
  2. 2.College of Electronic Science and EngineeringJilin UniversityChangchunChina

Personalised recommendations