Topic
Knowledge extraction
About: Knowledge extraction is a research topic. Over the lifetime, 20251 publications have been published within this topic receiving 413401 citations.
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TL;DR: The findings indicate that an association exists between the types of collaborative activities engaged in virtual settings and the effects such projects are perceived as having.
Abstract: Advances in information and communications technology have made possible collaborative activities in virtual settings. Virtual settings can significantly expand the knowledge resources available, yet they also create additional challenges to the already difficult activities of collaborating. The purpose of this research is to provide a better understanding of how collaborative activities in virtual settings enable the different parties to achieve their desired objectives by examining them from a knowledge management perspective. Three aspects of knowledge management-- knowledge transfer, knowledge discovery, and knowledge creation--are examined in the context of telemedicine projects. The findings indicate that an association exists between the types of collaborative activities engaged in virtual settings and the effects such projects are perceived as having. While this research focuses only on virtual collaborative activities in health care, it is likely that these findings are applicable to other industries engaged in such activities in virtual settings.
134 citations
TL;DR: The promising experimental performance on goal/corner event detection and sports/commercials/building concepts extraction from soccer videos and TRECVID news collections demonstrates the effectiveness of the proposed framework and indicates the great potential of extending the proposed multimedia data mining framework to a wide range of different application domains.
Abstract: In this paper, a subspace-based multimedia data mining framework is proposed for video semantic analysis, specifically video event/concept detection, by addressing two basic issues, i.e., semantic gap and rare event/concept detection. The proposed framework achieves full automation via multimodal content analysis and intelligent integration of distance-based and rule-based data mining techniques. The content analysis process facilitates the comprehensive video analysis by extracting low-level and middle-level features from audio/visual channels. The integrated data mining techniques effectively address these two basic issues by alleviating the class imbalance issue along the process and by reconstructing and refining the feature dimension automatically. The promising experimental performance on goal/corner event detection and sports/commercials/building concepts extraction from soccer videos and TRECVID news collections demonstrates the effectiveness of the proposed framework. Furthermore, its unique domain-free characteristic indicates the great potential of extending the proposed multimedia data mining framework to a wide range of different application domains.
134 citations
TL;DR: This work proposes the definition of four different dimensions, namely Pattern & Knowledge discovery, Information Fusion & Integration, Scalability, and Visualization, which are used to define a set of new metrics (termed degrees) in order to evaluate the different software tools and frameworks of SNA.
134 citations
TL;DR: Refuting the notion of technology as a replacement of knowledge, this paper focuses on a gap between them that needs to be bridged, and two models of knowledge are reviewed.
134 citations
TL;DR: The present article describes the range of text mining techniques that have been applied to scientific documents and divides 'automated reading' into four general subtasks: text categorization, named entity tagging, fact extraction, and collection-wide analysis.
134 citations