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An overview on consensus‐based approaches to processing collective inconsistency and knowledge integration
Author(s) -
Nguyen Van Du,
Truong Hai Bang,
Merayo Mercedes G.,
Nguyen Ngoc Thanh
Publication year - 2019
Publication title -
wiley interdisciplinary reviews: data mining and knowledge discovery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.506
H-Index - 47
eISSN - 1942-4795
pISSN - 1942-4787
DOI - 10.1002/widm.1311
Subject(s) - collective intelligence , knowledge integration , knowledge management , computer science , data science , knowledge sharing , process (computing) , knowledge engineering , operating system
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