Knowledge discovery and updating under the evolution of network formal contexts based on three-way decision

Non-redundant implicational base of formal context with constraints using SAT

An implicational base is knowledge extracted from a formal context. The implicational base of a formal context consists of attribute implications which are sound, complete, and non-redundant regarding to the formal context. Non-redundant means that each attribute implication in the implication base cannot be inferred from the others. However, sometimes some attribute implications in the implication base can be inferred from the others together with a prior knowledge. Regarding knowledge discovery, such attribute implications should be not considered as new knowledge and ignored from the implicational base. In other words, such attribute implications are redundant based on prior knowledge. One sort of prior knowledge is a set of constraints that restricts some attributes in data. In formal context, constraints restrict some attributes of objects in the formal context. This article proposes a method to generate non-redundant implication base of a formal context with some constraints which restricting the formal context. In this case, non-redundant implicational base means that the implicational base does not contain all attribute implications which can be inferred from the others together with information of the constraints. This article also proposes a formulation to check the redundant attribute implications and encoding the problem into satisfiability (SAT) problem such that the problem can be solved by SAT Solver, a software which can solve a SAT problem. After implementation, an experiment shows that the proposed method is able to check the redundant attribute implication and generates a non-redundant implicational base of formal context with constraints.

The Effect of the Knowledge Management Processes Capability on Innovation Activities in the Mexican Industry

Mexican industrial organizations focus their efforts on the development of new products and processes, including breakthrough technology. Few studies have focused on the analysis of this corporate effort. After an in-depth theoretical review, it has been detected that most of the research has been based on the study of knowledge management along its dimensions, and there has been no research that incorporates the capabilities of the knowledge management process and its effect on two activities that generate innovation. With the application of a Likert scale-type instrument to 279 industrial companies in Mexico, it was detected that there is a relationship and influence between the dimensions of knowledge acquisition (KAC), knowledge updating (KUP), knowledge protection (KP), knowledge conversion (KC) and knowledge application (KAP) in innovation-generating activities, such as research and development (R&D) and technology acquisition (TA). This was analyzed by the partial least squares (PLS) structural equation technique. Finally, it is important to highlight the contribution that the results of the study have in possible future research that can be carried out, for example, the analysis of the protection of knowledge and its impact on the industry of other countries.

Network rule extraction under the network formal context based on three-way decision

Knowledge discovery combined with network structure is an emerging field of network data analysis and mining. Three-way concept analysis is a method that can fit the human mind in uncertain decisions and analysis. In reality, when three-way concept analysis is placed in the background of a network, not only the three-way rules need to be obtained, but also the network characteristic values of these rules should be obtained, which is of great significance for concept cognition in the network. This paper mainly combines complex network analysis with the formal context of three-way decision. Firstly, the network formal context of three-way decision (NFC3WD) is proposed to unify the two studies mentioned above into one data framework. Then, the network weaken-concepts of three-way decision (NWC3WD) and their corresponding sub-networks are studied. Therefore, we can not only find out the network weaken-concepts but also know the average influence of the sub-network, as well as the influence difference within the sub-network. Furthermore, the concept logic of network and the properties of its operators are put forward, which lays a foundation for designing the algorithm of rule extraction. Subsequently, the bidirectional rule extraction algorithm and reduction algorithm based on confidence degree are also explored. Meanwhile, these algorithms are applied to the diagnosis examples of COVID-19 from which we can not only get diagnostic rules, but also know the importance of the population corresponding to these diagnostic rules in the network through network eigenvalues. Finally, experimental analysis is made to show the superiority of the proposed method.