A collaboration centric approach for building the semantic knowledge network for knowledge advantage machine
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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.
KeywordsKnowledge 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).
- 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.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
- 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.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
- 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
- 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