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This study proposes an application of cognitive maps in the representation of cognitive structures of the experts and assessment of their development/modification as a result of a (computer or expert system-assisted) learning process. It strives to identify information needed for the guidance of the process of creation and management of expert knowledge by formal modeling tools. Changes in experts' cognitive structures are assumed to stem from individual and collaborative (group-level) learning. The novel approach to assessing the outcomes of learning reflected as changes in the cognitive structures of experts or groups of experts, modeled by cognitive maps, does not assume any correct or desired outcome of the learning process to be known in advance. Instead, it identifies and analyzes the changes in (or robustness of) the constituents of the cognitive maps from different points of view and allows for quantifying and visualizing the actual effect of the learning. The proposed methodology can identify changes in cognitive diversity, causal structures in terms of causal relations and concepts, and the perceived importance of strategic issues over the learning period. It can also detect which cause–effect relationships have appeared/disappeared considering the pre-/post-mapping design. Thus, it provides an exploratory account on the changes in the cognitive structures of the expert(s) as a result of learning. The applicability of the proposed methods is illustrated in the assessment of the learning outcomes of a group of 71 graduate students who participated in an eight-week business simulation task. The results of the empirical analysis confirm the viability of the proposed methodology and indicate that the students' understanding of the utilized concepts and associated relationships in the decision-making process improved throughout the learning activity, ultimately showing that the course learning has considerably improved students' perception and knowledge. Based on the results, it can be concluded that the proposed approach has the potential to be effective in assessing learning outcomes in teaching–learning activities. • Technique for unsupervised assessment of expert knowledge development is proposed. • Causal maps represent current state of expert knowledge in different time points. • Desirability of learning (defined as changes in cognitive maps) is assessed ex-post. • Proposed tools enable quantification and visualization of the effects of learning. • Practical use of the tools in expert knowledge creation and management is shown. [ABSTRACT FROM AUTHOR]

Abstract: A cognitive map has been regarded as one of the most efficient ways to solve problems like lack and uncertainty of knowledge in many different domains. In this paper, we have designed an ontology-based knowledge management systems (KMS) which is capable of annotating cognitive maps. Then, an OntoCM (ontological cognitive map) framework is proposed to collaboratively share the annotated cognitive maps between businesses by using an OntoCM Repository. Thereby, in order to manipulate the annotated cognitive maps, we have defined OntoCM operations such as expansion, contradiction, augmentation, and screener. By employing several concept hierarchies as ontologies and simulating synthesis patterns on OntoCM, we have extracted potential relationships between the existing concepts, and expected that the relationships between the cognitive maps can help the businesses to improve understandability. [Copyright &y& Elsevier]

Abstract: Cognitive maps and fuzzy cognitive maps offer interesting and transparent modeling capabilities by functioning at a level of conceptual entities (nodes) and their relationships expressed either at the qualitative level of excitatory/inhibitory relationships or being further numerically quantified as encountered in fuzzy cognitive maps. While there has been a vast array of conceptual enhancements, a relatively less attention has been paid to the design of the maps especially when dealing with an algorithmic way of forming the map. The objective of this study is to offer a design strategy in which starting with experimental evidence in the form of numeric data, those data are transformed into a finite and small number of concepts (nodes) of the map and afterwards the connections of the map are estimated. We show that techniques of Granular Computing, especially fuzzy clustering are effectively used to form concepts (nodes) of well-articulated semantics. In the sequel, we show the use of global optimization in the form of Particle Swarm Optimization (PSO) to carry out calibration of the connections of the interrelationships between the nodes of the map. Numeric examples are concerned with the representation of time series and their visualization in the form of fuzzy cognitive maps. Further interpretation of the maps is also discussed. [Copyright &y& Elsevier]

Abstract: Web-based Intelligent Systems (WBIS), e.g. information retrieval, intelligent Web, and e-Learning, deal with tasks such as acquisition, representation, and management of knowledge about users. Based on a user profile, WBIS are able to behave according the particular needs of people through the intelligent adaptation of services, content, navigation interfaces, and many more factors. Thereby, the design of an approach devoted to meet such tasks is critical for achieving the goals pursued by WBIS. Therefore, in this article an approach oriented to elicit, state, and administrate user knowledge is outlined. This work introduces a user model, which supports the selection of teaching experiences that are delivered to students in the e-Learning field. The aim is to enhance the apprenticeship of individuals that receive lectures according to the user model that a Web-based Education System (WBES) holds about them. According to a sort of empirical outcomes, it is concluded that: “The success of WBIS is biased by the accurately acquisition, representation, and management of user knowledge fulfilled by the approach”. [Copyright &y& Elsevier]

Abstract: Due to the lack of an integral study about cognitive maps (CM) that focus on the causal phenomenon, this paper introduces the underlying concepts towards a holistic conceptual model, enhanced by a profile of several versions. We illustrate the use of CM through their application into the Web-based Education Systems (WBES). From the causal perspective, CM depict and simulate the systems dynamics based upon qualitative knowledge about a specific domain. A CM is a visual digraph that identifies the concepts of a given subject of analysis. CM show causal-effect relationships among the concepts and outline complex structures. This tool aims to predict the evolution of a model through causal inference. This kind of inference estimates the degree of significance of change of the concepts in the context of the whole system. The behavior of a CM is given away during iterations that update the variation of the concept state values until reach a stable point in a search space, a pattern of states or a chaotic region. The purpose of this research is to share its findings, depict the work done and promote the use of CM in a broad spectrum of domains. [Copyright &y& Elsevier]

In this paper, we investigate ways to apply news information on the Internet to the prediction of interest rates. We developed the Knowledge-Based News Miner (KBNMiner), which is designed to represent the knowledge of interest rate experts with cognitive maps (CMs), to search and retrieve news information on the Internet according to prior knowledge, and to apply the information, which is retrieved from news information, to a neural network model for the prediction of interest rates.This paper focuses on improving the performance of data mining by using prior knowledge. Real-world interest rate prediction data is used to illustrate the performance of the KBNMiner. Our integrated approach, which utilizes CMs and neural networks, has been shown to be effective in experiments. While the 10-fold cross validation is used to test our research model, the experimental results of the paired t-test have been found to be statistically significant. [Copyright &y& Elsevier]