Plausible Reasoning for the Problems of Cognitive Sociology
This paper discusses design process as a creative activity along with conceptual correlations of the semiotics developed by Charles Sanders Peirce. The central aim of this paper is to examine one of the most important concepts in Peirce’s theory related to design praxis: the concept of abduction. Abduction is the driving force behind creation and a way of producing new ideas. Peirce’s original concept is fundamental in order to maintain constant commitment to innovation required by design. To transmit messages in a creative way it is more efficient to intensely work with associations by similarity in order to obtain signs rich in information and analogies. Design communicates by all its constituent elements: shape, function, colour, material, technique, technology, etc. Therefore, signs of design share peculiar values of artistic signs as well as those of communicative ones. The associated information is as much aesthetic (shape) as it is semantic (content). The appropriation of Peircean concepts contributes to the understanding of the creative process, which in turn is crucial for understanding new possibilities by means of design.
Pattern structures, an extension of FCA to data with complex descriptions, propose an alternative to conceptual scaling (binarization) by giving direct way to knowledge discovery in complex data such as logical formulas, graphs, strings, tuples of numerical intervals, etc. Whereas the approach to classification with pattern structures based on preceding generation of classifiers can lead to double exponent complexity, the combination of lazy evaluation with projection approximations of initial data, randomization and parallelization, results in reduction of algorithmic complexity to low degree polynomial, and thus is feasible for big data.
Concept discovery is a Knowledge Discovery in Databases (KDD) research field that uses human-centered techniques such as Formal Concept Analysis (FCA), Biclustering, Triclustering, Conceptual Graphs etc. for gaining insight into the underlying conceptual structure of the data. Traditional machine learning techniques are mainly focusing on structured data whereas most data available resides in unstructured, often textual, form. Compared to traditional data mining techniques, human-centered instruments actively engage the domain expert in the discovery process. This volume contains the contributions to CDUD 2011, the International Workshop on Concept Discovery in Unstructured Data (CDUD) held in Moscow. The main goal of this workshop was to provide a forum for researchers and developers of data mining instruments working on issues with analyzing unstructured data. We are proud that we could welcome 13 valuable contributions to this volume. The majority of the accepted papers described innovative research on data discovery in unstructured texts. Authors worked on issues such as transforming unstructured into structured information by amongst others extracting keywords and opinion words from texts with Natural Language Processing methods. Multiple authors who participated in the workshop used methods from the conceptual structures field including Formal Concept Analysis and Conceptual Graphs. Applications include but are not limited to text mining police reports, sociological definitions, movie reviews, etc.
A hybrid approach to automated identification and monitoring of technology trends is presented. The hybrid approach combines methods of ontology based information extraction and statistical methods for processing OBIE results. The key point of the approach is the so called ‘black box’ principle. It is related to identification of trends on the basis of heuristics stemming from an elaborate ontology of a technology trend.
Concept Relation Discovery and Innovation Enabling Technology (CORDIET), is a toolbox for gaining new knowledge from unstructured text data. At the core of CORDIET is the C-K theory which captures the essential elements of innovation. The tool uses Formal Concept Analysis (FCA), Emergent Self Organizing Maps (ESOM) and Hidden Markov Models (HMM) as main artifacts in the analysis process. The user can define temporal, text mining and compound attributes. The text mining attributes are used to analyze the unstructured text in documents, the temporal attributes use these document’s timestamps for analysis. The compound attributes are XML rules based on text mining and temporal attributes. The user can cluster objects with object-cluster rules and can chop the data in pieces with segmentation rules. The artifacts are optimized for efficient data analysis; object labels in the FCA lattice and ESOM map contain an URL on which the user can click to open the selected document.
Since pre-school age, children rely on contextual information while generalizing information about new objects. It is still uncertain what underlies this inductive selectivity; whether it is associative learning, which depends on the numbers of features that an object has, or conceptual learning, which depends on the features’ content. In the first experiment, we varied the contextual information and found that 4-5-year-olds rely more on contextual features of the object (shape and colour of the background), but not on spatial ones (location). In the second experiment we varied the combination of context features and showed that, given a lack of information about an object (shape only), children rely on contextual spatial features more than on the object’s features. Moreover, they prefer not to rely on contextual information at all if the object’s information was modified (same shape but different colour). Together, these results indicate the dependence of inductive selectivity on conceptual learning, not only associative learning.
This proceedings publication is a compilation of selected contributions from the “Third International Conference on the Dynamics of Information Systems” which took place at the University of Florida, Gainesville, February 16–18, 2011. The purpose of this conference was to bring together scientists and engineers from industry, government, and academia in order to exchange new discoveries and results in a broad range of topics relevant to the theory and practice of dynamics of information systems. Dynamics of Information Systems: Mathematical Foundation presents state-of-the art research and is intended for graduate students and researchers interested in some of the most recent discoveries in information theory and dynamical systems. Scientists in other disciplines may also benefit from the applications of new developments to their own area of study.
This article is talking about state management and cultural policy, their nature and content in term of the new tendency - development of postindustrial society. It mentioned here, that at the moment cultural policy is the base of regional political activity and that regions can get strong competitive advantage if they are able to implement cultural policy successfully. All these trends can produce elements of new economic development.