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About 10,000 papers for “Knowledge acquisition”

Knowledge Acquisition

Brian R. Gaines · 1992

This article develops a model of organizational knowledge acquisition in terms of modern psychological, sociological, economic and management theories by deconstructing the terms involved: an organization as a collective agent having goals and capabilities to achieve them; knowledge as the hidden state variables imputed to an agent as the basis of its capabilities; and acquisition as the reproduction of dispositions. This form of model enables one to relate the knowledge processes involved to existing models of organizational processes, and to understand such phenomena as knowledge economics and knowledge management. The breadth of the notion of organization encompasses markets, firms and societies; the operational definition of knowledge clarifies its role and the utility of the notion; and the focus on reproduction of dispositions in knowledge acquisition enables the management of knowledge acquisition to be analyzed. ______________________________________________________________________________________ 1.

Cited by 0

Knowledge acquisition.

Farah Nabil (12322485), Kira Grachev (16528294), Ángel Gasch-Gallén (11681755) et al. · Figshare · 2023

<div><p>Men who have sex with men (MSM) experience a high degree of discrimination and marginalization in Senegal. Homophobia is pervasive in Senegalese society at the cultural, religious, and political levels. Its effects are reflected in the disproportionately high levels of depression, anxiety, and substance abuse among men who have sex with men when compared to the general population. Given the widespread stigma and the lack of structural support, healthcare providers play a critical role in reconciling the physical and psychological needs of men who have sex with men. This led to the design of a training program that aimed to improve the capacity of healthcare providers to deliver MSM-competent psychosocial care. The training was delivered virtually to 37 Senegal-based nurses and physicians. The program was quantitatively and qualitatively evaluated using pre- and post-testing. The findings demonstrate a general post-training increase in knowledge acquisition (9. 23%, p-value = 0.0021) and a 6.39% reduction in homophobia, which was statistically significant (p = 0.0376); with male providers outperforming female providers, and physicians outperforming nurses. This demonstrates the effectiveness and applicability of the program to support the psychosocial needs of men who have sex with men, and its capacity for future and broader implementation among healthcare providers.</p></div>

Cited by 0Open access

Knowledge Acquisition

A Gil, Eric Melz, Funding Numbers et al. · 1996

Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching exiting data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimated or any other aspect of this collection of information, including suggestings for reducing this burden to Washington Headquarters Services, Directorate for Information Operations

Cited by 0

Knowledge acquisition

Yi‐Hwa Liou · ACM SIGMIS Database the DATABASE for Advances in Information Systems · 1992

The application of expert systems has increased drastically in the last decade. The power of these systems derives from the knowledge they possess, rather than from the inference mechanism that they employ. To ensure the performance of an expert system, the acquisition of knowledge becomes a vital task in the development process. This article discusses issues related to people involved in the knowledge acquisition task, reviews techniques to acquire knowledge, and presents a methodology that offers a structured approach to knowledge acquisition. Trends in knowledge acquisition are discussed.

Cited by 56Open access

Knowledge Acquisition

Robert AlfredBenfer, Edward Brent, Louanna Furbee · Expert Systems · 1991

Cited by 5

Knowledge Acquisition

Magdi N. Kamel · Wiley Encyclopedia of Computer Science and Engineering · 2007

Abstract Knowledge acquisition is the process by which problem‐solving expertise is obtained from some knowledge source, usually a domain expert. This knowledge can then be implemented into an expert system program that can provide expert assistance to nonexperts when and where a human expert is not available. Traditionally knowledge acquisition is accomplished through a series of long and intensive interviews between a knowledge engineer, who is a computer specialist, and a domain expert, who has superior knowledge in the domain of interest. The difficulty, time, and cost of manual knowledge acquisition, however, have stimulated research in developing machine learning approaches that autonomously acquire knowledge from data sources without the assistance of humans. This article presents an in‐depth overview of knowledge acquisition by discussing the fundamental concepts of knowledge, types of knowledge, the process of knowledge acquisition, and knowledge acquisition methods. The article presents a classification that divides knowledge acquisition methods into three categories: manual methods, combined manual and automated methods, and automated methods. Manual methods discussed include interviews, task‐based methods, and expert self‐elicitation. Combined manual and automated methods discussed include expert‐driven and knowledge engineer‐driven approaches. Automated methods presented include learning by induction, neural networks, genetic algorithms, and analogical and case‐based reasoning. The article also addresses knowledge presentation and discusses eight of the most common representation methods: logic, production rules, frames, semantic networks, objects‐attributes‐value triplets, scripts, decision tables, and decision trees. The article concludes with a discussion of validation and verification of acquired knowledge.

Cited by 5

Knowledge Acquisition

The MIT Press eBooks · 2024

As should already be clear, for LEIAs to operate with understanding across many domains, they need knowledge-and a lot of it.By knowledge, we mean data that has been translated into the unambiguous, ontologically grounded metalanguage of the LEIA's knowledge bases.Previous chapters have shown how agents can acquire knowledge through learning.This chapter describes manual and semiautomatic methods of knowledge acquisition within the DEKADE infrastructure.These methods facilitate the acquisition of the bootstrapping knowledge that is needed to enable LEIAs to learn automatically with good results.The focus is on efficient methodologies that take the complexity of language and the world seriously.Before we begin, a common misconception must be addressed.The need for manual work on knowledge in AI is not unique to knowledge-based systems.Data-driven systems also rely heavi ly on manual labor in the form of annotating and cleaning training datasets, tweaking parameters, and even recording application-specific responses, such as answers to typical questions.According to The Economist, as of January 2020, data preparation still claimed over three quarters of the time allocated to machine learning proj ects. 1 And according to Stasha (2021), Alexa Smart Assistant and Echo products perform so well because thousands of employees are working on optimizing these specific products.Similarly impressive human workforce outlays were needed to prepare IBM's Watson to play Jeopardy!However, despite Watson's spectacular win, it could not handle the ambitious follow-up applications it was set to (Lohr, 2021).In short, if one chooses to think of knowledge as a prob lem, then data-driven approaches to AI share the prob lem.However, since LEIAs are on a dif fer ent path toward artificial intelligence than the data-driven majority, it stands to reason that the focus of manual labor is dif fer ent as well.

Cited by 0Open access

Keywords: Knowledge Acquisition, Knowledge Collaboration

Joseph Mceniry, David Bjorne · 2008

ABSTRACT: This paper describes the techniques, which are being developed and used by Bevilacqua Research Corporation (BRC), to address the cooperative development and reuse of knowledge for intelligent simulations. The work is based on the use of the draft proposed American National Standards (dpANS) Conceptual Graph standard that defines a Conceptual Graph Interchange Format (CGIF). This standard, while originated for Conceptual Graphs, offers the flexibility and robustness to describe knowledge. The knowledge description can then be utilized in any application that can understand the dpANS standard. The paper summarizes the state of the current standardization efforts, then discusses parsing CGIF, and concludes by discussing knowledge applications and some implications of the standardization of knowledge representations. 1.

Cited by 0