An overview on consensus‐based approaches to processing collective inconsistency and knowledge integration

Overview

Ngoc Thanh Nguyen, Mercedes G. Merayo, Hai Bang Truong, Van Du Nguyen

Published Online: Apr 04 2019

DOI: 10.1002/widm.1311

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The possibility of connecting billions of people around the world through network is the main characteristic of cyber‐physical systems production. To date, social web, virtual communities, and information sharing have been considered as additional sources of information, knowledge which is often referred to as the wisdom of crowds for information retrieval. In these cases, there exists a need to integrate all individual knowledge states originating from different sources concerning some common problems in the real world. Knowledge integration is considered as the key to improve decision quality and enhance the core competency of organizations. For such a task, the most important issue is processing inconsistency of knowledge. The first part of the paper presents an overview of consensus‐based approaches to processing collective inconsistency arising in the integration process. In addition, some issues concerning knowledge integration such as multi‐level integration and knowledge increase in a collective are also devoted. Furthermore, we will investigate the research problem concerning the role of collective knowledge in Collective Intelligence. This article is categorized under: Technologies > Data Preprocessing Fundamental Concepts of Data and Knowledge > Knowledge Representation Fundamental Concepts of Data and Knowledge > Big Data Mining

References

Akhavan,, P., Ghojavand,, S., & Abdali,, R. (2012). Knowledge sharing and its impact on knowledge creation. Journal of Information %26 Knowledge Management (JIKM), 11, 101–113.
Armstrong,, J. S. (2001). Combining forecasts. In Principles of forecasting (pp. 417–439). Amsterdam: Kluwer Academic Publisher.
Beer,, M., D`inverno,, M., Luck,, M., Jennings,, N., Preist,, C., & Schroeder,, M. (1999). Negotiation in multi‐agent systems. The Knowledge Engineering Review, 14, 285–289.
Berg,, J., Forsythe,, R., Nelson,, F., & Rietz,, T. (2008). Results from a dozen years of election futures markets research. In Handbook of experimental economics results (pp. 742–751). Amsterdam, The Netherlands: Elsevier.
Blattberg,, R. C., & Hoch,, S. J. (1990). Database models and managerial intuition: 50% model+ 50% manager. Management Science, 36, 887–899.
Brabham,, D. C. (2013). Crowdsourcing. The MIT press essential knowledge series. Cambridge, England: Massachusetts Institute of Technology.
Cao,, X., Guo,, X., Liu,, H., & Gu,, J. (2015). The role of social media in supporting knowledge integration: A social capital analysis. Information Systems Frontiers, 17, 351–362.
Chowdhury,, N. A., Khatun,, M., & Hashem,, M. M. A. (2007). On integrating fuzzy knowledge using a novel evolutionary algorithm. In Proceedings of 2007 10th international conference on computer and information technology (pp. 1–6).
Cowgill,, B., Wolfers,, J., & Zitzewitz,, E. (2009). Using Prediction Markets to Track Information Flows: Evidence from Google. In Proceeding of AMMA 2009 (pp. 3), Berlin: Springer.
Day,, W. H. E. (1988). The consensus methods as tools for data analysis. In Proceedings of IFC`87: Classifications and related methods of data analysis (pp. 317–324).
dos Santos,, C. T., & Bazzan,, A. L. (2005). Integrating knowledge through cooperative negotiation—A case study in bioinformatics. In International workshop on autonomous intelligent systems: Agents and data mining (pp. 277–288). Berlin, Heidelberg: Springer.
Duong,, T. H., Nguyen,, N. T., & Jo,, G. S. (2008). A method for integration of WordNet‐based ontologies using distance measures. In Proceedings of KES 2008 (pp. 210–219).
Foss,, N. J., Husted,, K., & Michailova,, S. (2010). Governing knowledge sharing in organizations: Levels of analysis, governance mechanisms, and research directions. Journal of Management Studies, 47, 455–482.
Grant,, R. M. (1996). Toward a knowledge‐based theory of the firm. Strategic Management Journal, 17, 109–122.
Hecker,, A. (2012). Knowledge beyond the individual? Making sense of a notion of collective knowledge in organization theory. Organization Studies, 33, 423–445.
Hernes,, M., & Bytniewski,, A. (2017). Knowledge integration in a manufacturing planning module of a cognitive integrated management information system. In Proceedings of ICCCI 2017 (pp. 34–43).
Herrera‐Viedma,, E., Cabrerizo,, F. J., Kacprzyk,, J., & Pedrycz,, W. (2014). A review of soft consensus models in a fuzzy environment. Information Fusion, 17, 4–13.
Kacprzyk,, J., & Zadrozny,, S. (2010). Soft computing and web intelligence for supporting consensus reaching. Soft Computing, 14, 833–846.
Kim,, M. J., Han,, I., & Lee,, K. C. (2004). Hybrid knowledge integration using the fuzzy genetic algorithm: Prediction of the Korea stock price index. Intelligent Systems in Accounting, Finance and Management, 12, 43–60.
Kolaczek, G., Kuchtiak‐Pieczynska, A., Juszczyszyn, K., Grzech, A., Katarzynak, R., Nguyen, N.T. (2005). A Mobile agent approach to intrusion detection in network systems. In: Proceedings of KES 2005, lecture notes in artificial intelligence 3682 (pp. 514–519).
Kozierkiewicz‐Hetmanska,, A. (2012). Comparison of one‐level and two‐level consensuses satisfying the 2‐optimality criterion. In Proceedings of ICCCI 2012 (pp. 1–10).
Kozierkiewicz‐Hetmanska,, A. (2017). The analysis of expert opinions` consensus quality. Information Fusion, 34, 80–86.
Kozierkiewicz‐Hetmanska,, A., & Pietranik,, M. (2017a). Assessing the quality of a consensus determined using a multi‐level approach. In Proceedgins of INISTA 2017 (pp. 131–136).
Kozierkiewicz‐Hetmanska,, A., & Pietranik,, M. (2017b). The knowledge increase estimation framework for ontology integration on the concept level. Journal of Intelligent and Fuzzy Systems, 32, 1161–1172.
Kurvers,, R. H., Herzog,, S. M., Hertwig,, R., Krause,, J., Carney,, P. A., Bogart,, A., … Wolf,, M. (2016). Boosting medical diagnostics by pooling independent judgments. Proceedings of the National Academy of Sciences of the United States of America, 113, 8777–8782.
Lang,, M., Bharadwaj,, N., & Di Benedetto,, C. A. (2016). How crowdsourcing improves prediction of market‐oriented outcomes. Journal of Business Research, 69, 4168–4176.
Levy,, P. (1997). Collective intelligence: Mankind`s emerging world in cyberspace. Cambridge, MA: Perseus Books.
Liu,, Y. F., Yuan,, Y. J., & Zhao,, C. G. (2012). Study on knowledge integration social network of scientific research team in university. Advanced Materials Research, 479‐481, 623–627.
Lu,, Y. (2017). Industry 4.0: A survey on technologies, applications and open research issues. Journal of Industrial Information Integration, 6, 1–10.
Maleszka,, M. (2017). Particle swarm of agents for Heterogenous knowledge integration. In Proceedings of ICCCI 2017 (pp. 54–62).
Maleszka,, M., Mianowska,, B., & Nguyen,, N. T. (2013). A method for collaborative recommendation using knowledge integration tools and hierarchical structure of user profiles. Knowledge‐Based Systems, 47, 1–13.
Maleszka,, M., & Nguyen,, N. T. (2015). Integration computing and collective intelligence. Expert Systems with Applications, 42, 332–340.
Mohan,, K., Jain,, R., & Ramesh,, B. (2007). Knowledge networking to support medical new product development. Decision Support Systems, 43, 1255–1273.
Nguyen,, N. T. (2007). A method for ontology conflict resolution and integration on relation level. Cybernetics and Systems, 38, 781–797.
Nguyen,, N. T. (2008a). Advanced methods for inconsistent knowledge management. London, England: Springer.
Nguyen,, N. T. (2008b). Inconsistency of knowledge and collective intelligence. Cybernetics and Systems, 39, 542–562.
Nguyen,, N. T., & Truong,, H. B. (2010). A consensus‐based method for fuzzy ontology integration. In Proceedings of ICCCI 2010 (pp. 480–489).
Razmerita,, L., Kirchner,, K., & Nabeth,, T. (2014). Social Media in Organizations: Leveraging personal and collective knowledge processes. Journal of Organizational Computing and Electronic Commerce, 24, 74–93.
Russell,, P. (2000). The global brain awakens: Our next evolutionary leap. Shaftesbury: Element Books Ltd.
Surowiecki,, J. (2005). The wisdom of crowds. New York, NY: Doubleday/Anchor.
Tajuddin,, N. I. I., Abdulllah,, R., Jusoh,, Y. Y., & Jabar,, M. A. (2018). Towards developing conceptual model for knowledge integration through social media among SMEs. Advanced Science Letters, 24, 5224–5229.
Tzabbar,, D., Aharonson,, B. S., & Amburgey,, T. L. (2013). When does tapping external sources of knowledge result in knowledge integration? Research Policy, 42, 481–494.
Wang,, S., & Noe,, R. A. (2010). Knowledge sharing: A review and directions for future research. Human Resource Management Review, 20, 115–131.

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