Search

This paper presents the results based on the research from the integration of advanced methods formerly used in different disciplines and its practical implementation. The integration focusses on content description on the one hand and on computational methods on the other hand. The result of the integration enables advanced knowledge mining workflows, spanning the knowledge of many conceptual knowledge implementations. The integration includes classifications implementing conceptual knowledge, content and classification based verbal descriptions, and methods used with knowledge mining computation. Conceptual knowledge is a rich source of high quality if used appropriately. The value of conceptual knowledge references even increases when considering verbal descriptions, which are available for most implementations. The goal of this research is to integrate conceptual knowledge and associated verbal description in the knowledge mining process. In the context of complex knowledge resources, referencing to multi-disciplinary long-term knowledge, the exploitation can lead to much improved mining results and new insights. The integration goes along with challenges for the creation of resources and design of High End Computation (HEC) workflows. [ABSTRACT FROM AUTHOR]

This paper presents research based on the new Content Factor (CONTFACT) applied to long-term knowledge resources, which are continuously in development. The CONTFACT methodology was especially created for data description and analysis complementary to previously existing methods. The Content Factor method can be applied to arbitrary data and content and it can be adopted for many purposes like data analysis and knowledge discovery. The goal of this research is to use the new methodology in order to create additional data-centric description to multi-disciplinary long-term knowledge resources. The new methodology supports flexible ways for data description and analysis and can be used with huge structured and even unstructured data resources, e.g., with Integrated Information and Computing System (IICS) components and High End Computing (HEC) resources. [ABSTRACT FROM AUTHOR]

This paper presents the recent results of the integration of computational and conceptual solutions for the complex case of environmental information management. The solution for the major goal of creating and developing long-term multi-disciplinary knowledge resources and conceptual and computational support was achieved by implementing and integrating key components. The key components are long-term knowledge resources providing required structures for universal knowledge creation, documentation, and preservation, universal multi-disciplinary and multi-lingual conceptual knowledge and classification, especially, references to Universal Decimal Classification (UDC), sustainable workflows for environmental information management, and computational support for dynamical use, processing, and advanced scientific computing with Integrated Information and Computing System (IICS) components and High End Computing (HEC) resources. [ABSTRACT FROM AUTHOR]

This paper presents the up-to-date results concerning the integration of scientific classification and computation deploying long-term knowledge resources. The knowledge resources are the central component in universal multi-disciplinary and multi-lingual knowledge management. While the research focus is on universal knowledge creation, documentation, and preservation, the architecture allows efficient dynamical use and processing as well as computational utilisation, e.g., in advanced scientific computing with Integrated Information and Computing System (IICS) components and High End Computing (HEC) resources. The knowledge resources' goal is to address a major gap for long-term knowledge and to provide structure, references, and classification for any type of data, knowledge, and context. Based on the implementation, the case study depicts concepts of references to Universal Decimal Classification (UDC) views, e.g., for workflows. The key results are benefits for knowledge management and documentation, the sustainable integration of long-term documentation, data, and workflow vitality for improved knowledge gathering, computational purposes, and evaluation based on big data. [ABSTRACT FROM AUTHOR]