This paper describes a possible extension of an existing model-based system called KnowWeb for support of knowledge management within company to incorporate knowledge discovered from data available in company&apos;s databases.

Cited by 0

Knowledge Discovery and Knowledge Transfer

Fangming Ye, Zhaobo Zhang, Krishnendu Chakrabarty et al. · 2016

Cited by 1

Geographic Knowledge Discovery

Harvey J. Miller · Encyclopedia of GIS · 2008

Cited by 5

Knowledge Data Discovery

Jaroš, Ondřej · Brno University of Technology Digital Library (Brno University of Technology) · 2008

Tato bakalářská práce se zabývá problematikou získávání znalostí z databází. Jsou zde podrobně probrány metody pro dolování dat z databází. Konkrétně se tato práce zaměřuje na metodu získávání znalostí pomocí Asociačních pravidel a to za pomocí algoritmu Apriori. Cílem práce bylo implementovat vybranou metodu a její funkčnost ověřit na vybraném vzorku dat. Aplikace je implementována v programovacím jazyce Java a vzorek dat, nad kterými metoda pracuje je uložen v souboru XML. Nejedná se tedy o klasické získávání znalostí z databáze, nýbrž z XML souboru. Informace pro spuštění dolovací úlohy jsou načítány z konfiguračního(systémového) XML dokumentu. Získané znalosti jsou poté ukládány do systémového XML dokumentu.

Cited by 0

Automated Knowledge Discovery

Ben Williamson · 2021

Big data and AI have become integral to new scientific approaches to the understanding of learning and educational processes. Data-intensive forms of scientific discovery and knowledge production are promoted extensively by “learning scientists” and “learning engineers” with support from influential international institutions. Drawing extensively on technical engineering advances in knowledge discovery in disciplines including psychology, cognitive science, neuroscience, and genomics, these developments exemplify the emergence of a new science of policy-relevant research and development in education. This chapter, following the science and technology studies’ tradition of unpacking the expert practices and material apparatuses by which science makes its objects legible for intervention, provides an analysis of automated knowledge discovery as an emerging epistemology and a set of data practices in educational research. By mapping out the imaginaries, infrastructures, and practices of educational data science, the chapter traces the implications of applying learning science and engineering insights to the tasks of educational policy and practice.

Cited by 2Open access

Knowledge Discovery Systems

Irma Becerra‐Fernandez, Rajiv Sabherwal, Richard Kumi · Knowledge management · 2023

Cited by 0

Knowledge discovery methods

Stefan Smolnik · 2003

Coping with complex and dynamic environments forces companies to invest in the resource knowledge for the maintenance of their operational reliability; they have to create and to renew the resource knowledge continuously. Elementary activities of knowledge management comprise collecting, organizing, connecting, sharing, and applying of knowledge. A necessary prerequisite before the outcome of knowledge can be deployed and activities can be utilized is the availability of knowledge. Thus, creation and discovery of knowledge play a decisive role to increase the availability of knowledge assets of a company, the growth of corporate knowledge, the capacity to act, and finally, the effectiveness of companies. This paper contributes to the understanding of knowledge management through an exploration of different approaches of knowledge discovery methods. A detailed literature study forms the basis of this contribution and thus, it results in a selection of several approaches of different research domains, which seems to be appropriated for our purpose. In detail, we focus on following main approaches: developmental psychology and epistemologies, like the ways small children learn to discover the world (following Piaget) or inquiring systems (following Churchman), artificial intelligence methods, like data mining, the discovery of general world knowledge in texts, and the creation of knowledge structures and reusable structured semantic link networks. The findings comprise delimitations of specific paradigms, characteristics, and fields of application of knowledge discovery methods as well as similarities and interdependencies. We argue that the exploration of these methods is a necessary prerequisite to fill knowledge gaps , as the importance of the resource knowledge as a success factor increases continuously.

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Adversarial Knowledge Discovery

David B. Skillicorn · IEEE Intelligent Systems · 2009

Cited by 3