Topic
Knowledge acquisition
About: Knowledge acquisition is a research topic. Over the lifetime, 11809 publications have been published within this topic receiving 242323 citations.
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TL;DR: This approach seems to be of fundamental importance to artificial intelligence (AI) and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, decision support systems, inductive reasoning, and pattern recognition.
Abstract: Rough set theory, introduced by Zdzislaw Pawlak in the early 1980s [11, 12], is a new mathematical tool to deal with vagueness and uncertainty. This approach seems to be of fundamental importance to artificial intelligence (AI) and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, decision support systems, inductive reasoning, and pattern recognition.
7,185 citations
01 Mar 1998
TL;DR: The paradigm shift from a transfer view to a modeling view is discussed and two approaches which considerably shaped research in Knowledge Engineering are described: Role-limiting Methods and Generic Tasks.
Abstract: This paper gives an overview of the development of the field of Knowledge Engineering over the last 15 years. We discuss the paradigm shift from a transfer view to a modeling view and describe two approaches which considerably shaped research in Knowledge Engineering: Role-limiting Methods and Generic Tasks. To illustrate various concepts and methods which evolved in recent years we describe three modeling frameworks: CommonKADS, MIKE and PROTEGE-II. This description is supplemented by discussing some important methodological developments in more detail: specification languages for knowledge-based systems, problem-solving methods and ontologies. We conclude by outlining the relationship of Knowledge Engineering to Software Engineering, Information Integration and Knowledge Management.
3,406 citations
Book•
03 Oct 2013
TL;DR: This book contains tutorial overviews and research papers on contemporary trends in the area of machine learning viewed from an AI perspective, including learning from examples, modeling human learning strategies, knowledge acquisition for expert systems, learning heuristics, discovery systems, and conceptual data analysis.
Abstract: This book contains tutorial overviews and research papers on contemporary trends in the area of machine learning viewed from an AI perspective. Research directions covered include: learning from examples, modeling human learning strategies, knowledge acquisition for expert systems, learning heuristics, discovery systems, and conceptual data analysis.
2,824 citations
Book•
17 Aug 1999
TL;DR: This chapter discusses knowledge representation, meaning, purpose, context, and agents in the context of ontology, as well as some examples of knowledge acquisition and sharing.
Abstract: 1. Logic. 2. Ontology. 3. Knowledge Representation. 4. Processes. 5. Purposes, Contexts, And Agents. 6. Knowledge Soup. 7. Knowledge Acquisition And Sharing. Appendixes: Appendix A: Summary Of Notations Appendix B: Sample Ontology. Appendix C: Extended Example. Answers To Selected Exercises. Bibliography. Name Index. Subject Index. Special Symbols.
2,725 citations
TL;DR: The results indicate that the social interaction and network ties dimensions of social capital are indeed associated with greater knowledge acquisition, but that the relationship quality dimension is negatively associated with knowledge acquisition.
Abstract: Employing a sample of 180 entrepreneurial high-technology ventures based in the United Kingdom, we examine the effects of social capital in key customer relationships on knowledge acquisition and knowledge exploitation. Building on the relational view and on social capital and knowledge-based theories, we propose that social capital facilitates external knowledge acquisition in key customer relationships and that such knowledge mediates the relationship between social capital and knowledge exploitation for competitive advantage. Our results indicate that the social interaction and network ties dimensions of social capital are indeed associated with greater knowledge acquisition, but that the relationship quality dimension is negatively associated with knowledge acquisition. Knowledge acquisition is, in turn, positively associated with knowledge exploitation for competitive advantage through new product development, technological distinctiveness, and sales cost efficiency. Further, our results provide evidence that knowledge acquisition plays a mediating role between social capital and knowledge exploitation. Copyright © 2001 John Wiley & Sons, Ltd.
2,556 citations