Collective intelligent information and database systems

Collective intelligence is most often understood as group intelligence which arises on the basis of intelligences of the group members. On the one hand, Newell [19] defined an intelligent collective as a social system, which is capable to act, even approximately, as a single rational agent. On the other hand, Lévy [11] understood collective intelligence as an intelligence that emerges from the collaboration and competition of many individuals; an intelligence that seemingly has a mind of its own. These definitions refer to cognitive systems. From the computational and artificial intelligence point of view, we can think about a collective as a set of autonomous units working on some common task, for example, a multi-agent system. We can say that the collective is intelligent if it can make a use of the intelligences of its members for solving some problem, for example, a decision making problem. Thus Computational Collective Intelligence (CCI) should provide computational methods which are based on the collective intelligence aspects and which use the computational techniques for solving these problems. Regarding knowledge engineering, computational collective intelligence provides methods and techniques for determining the knowledge of a collective as a whole on the basis of collective members’ knowledge. The need for processing collective knowledge is quickly increasing because of the very fast development of Internet, social networks and distributed databases. It is obvious that knowledge originating from autonomous sources for the same subject is very often inconsistent. Therefore, the aspects of inconsistency processing and integration computing are very important. Knowledge engineering [9] plays a relevant role in CCI since it is necessary to use its techniques in order to represent individual information. In fact, there are studies advocating that scientific knowledge is essentially collective knowledge [23]. In this line, recent studies state that it is very important that different individuals provide orthogonal, highly unrelated, and possible contradictory knowledge to the collectivity. In other words, “the higher the inconsistency, the better the quality of collective knowledge” [20]. Another relevant interaction with knowledge engineering is ontology matching and integration [27], a fundamental activity in order to collect and process information in CCI. Since it is well known that this process is both time and resource-consuming, it is necessary to provide advanced architectures and platforms to reduce these costs [15] and good algorithms and frameworks to efficiently integrate ontologies [16,21]. Although machine learning and data mining are two independent fields of work, there are frequent interactions between them [18]. Interestingly enough CCI uses machine learning and data mining in its solutions but there are also numerous applications of CCI to improve machine learning and data mining processes. A first issue concerns the representation of data in machine learning, given the fact that efficient algorithms strongly rely on a good structure of data. Actually, the research community organizes contests to compare existing approaches and to identify future challenges [7] and, as a result, new highly efficient methods, beating previous learning methods, are developed to improve representation learning [30]. Given the huge amount of data generated by current information systems, it