Student Modelling and Knowledge Acquisition Process in Complex Nature Domains

2018 ◽  
pp. 89-106
Author(s):  
Eleandro Maschio ◽  
Carolina Moreira
Keyword(s):  
Knowledge Acquisition ◽  
Complex Nature ◽  
Student Modelling ◽  
Acquisition Process

Beyond Classification

2008 ◽  
pp. 1855-1876
Author(s):  
Anna Olecka
Keyword(s):  
Data Mining ◽  
Credit Risk ◽  
Credit Card ◽  
Classification Scheme ◽  
Credit Scoring ◽  
Complex Nature ◽  
Acquisition Process ◽  
Business Objective ◽  
Simplifying Assumptions ◽  
History Of

This chapter will focus on challenges in modeling credit risk for new accounts acquisition process in the credit card industry. First section provides an overview and a brief history of credit scoring. The second section looks at some of the challenges specific to the credit industry. In many of these applications business objective is tied only indirectly to the classification scheme. Opposing objectives, such as response, profit and risk, often play a tug of war with each other. Solving a business problem of such complex nature often requires a multiple of models working jointly. Challenges to data mining lie in exploring solutions that go beyond traditional, well-documented methodology and need for simplifying assumptions; often necessitated by the reality of dataset sizes and/or implementation issues. Examples of such challenges form an illustrative example of a compromise between data mining theory and applications.

Beyond Classification

2011 ◽  
pp. 139-161
Author(s):  
Anna Olecka
Keyword(s):  
Data Mining ◽  
Credit Risk ◽  
Credit Card ◽  
Classification Scheme ◽  
Credit Scoring ◽  
Complex Nature ◽  
Acquisition Process ◽  
Business Objective ◽  
Simplifying Assumptions ◽  
History Of

This chapter will focus on challenges in modeling credit risk for new accounts acquisition process in the credit card industry. First section provides an overview and a brief history of credit scoring. The second section looks at some of the challenges specific to the credit industry. In many of these applications business objective is tied only indirectly to the classification scheme. Opposing objectives, such as response, profit and risk, often play a tug of war with each other. Solving a business problem of such complex nature often requires a multiple of models working jointly. Challenges to data mining lie in exploring solutions that go beyond traditional, well-documented methodology and need for simplifying assumptions; often necessitated by the reality of dataset sizes and/or implementation issues. Examples of such challenges form an illustrative example of a compromise between data mining theory and applications.

Nested Ripple Down Rules (NRDR) as a Design Assistant in Mechanical Dimensional Tolerancing

2010 ◽  
Author(s):  
Ramsey F. Hamade ◽  
Ali H. Ammouri ◽  
G. Beydoun
Keyword(s):  
Knowledge Management ◽  
Knowledge Acquisition ◽  
Management System ◽  
Knowledge Management System ◽  
Design Knowledge ◽  
Mathematical Functions ◽  
Acquisition Process ◽  
Engineering Standards ◽  
To Receive ◽  
Ripple Down Rules

The dimensional tolerancing knowledge management system presented in this paper uses Nested Ripple down Rules (NRDR) targeted towards incrementally capturing expert design knowledge. A demonstrated example of such captured knowledge is that which human designers utilize in order to specify dimensional tolerances on shafts and mating holes in order to meet desired classes of fit as set by relevant engineering standards. In doing so, NRDR interface was designed to receive mathematical functions with their specifications prior and during the KA process. This is necessary to be able to exploit relationships among several classes with respect to certain numerical features of the cases in order to accelerate the convergence of the NRDR knowledge acquisition process by generating artificial cases which are likely to trigger the addition of exception rules. The incorporation of equations constitutes a novel contribution to the field of knowledge acquisition with NRDR. The developed dimensional tolerancing knowledge management system would help mechanical designers become more effective in the time-consuming tolerancing process of their designs in the future.

Knowledge Geometry

2015 ◽  
Vol 14 (04) ◽  
pp. 1550028 ◽  
Author(s):  
Flávio Luis de Mello ◽  
Roberto Lins de Carvalho
Keyword(s):  
Knowledge Management ◽  
Information Processing ◽  
Knowledge Acquisition ◽  
Information Management ◽  
Spatial Representation ◽  
Geometric Approach ◽  
Alternative Theory ◽  
Acquisition Process ◽  
Evaluation Systems ◽  
Processing Information

This paper aims to present what we call knowledge geometry, an alternative theory for spatial representation of features related to information processing, information management, and knowledge management. It is a unique geometric approach for representing intuition, reification, interpretation, and deduction processes, as well as their relations. We employ the concept of cultural filter and use what we call real, conceptual, and symbolic planes in order to support transformations which occur along the perception of a phenomenon. After that, we discuss the use of evaluation systems to judge concepts and also the use of semantic systems as a communication language. Finally, a framework of the knowledge acquisition process in the field of the proposed theory is offered, proving the feasibility of its automation.

scholarly journals Analysis of empirical knowledge acquisition process in optimal design of multi-disciplinary system

2015 ◽  
Vol 81 (830) ◽  
pp. 15-00244-15-00244
Author(s):  
Yutaka NOMAGUCHI ◽  
Hiroyuki NAKAYAMA ◽  
Haruki INOUE ◽  
Koutaro TODA ◽  
Kikuo FUJITA
Keyword(s):  
Optimal Design ◽  
Knowledge Acquisition ◽  
Empirical Knowledge ◽  
Acquisition Process ◽  
Disciplinary System

scholarly journals Validating the Knowledge Acquisition Process Metrics in Content Management Systems

2019 ◽  
Vol 9 (1S3) ◽  
pp. 257-262
Keyword(s):  
Knowledge Acquisition ◽  
Software Metrics ◽  
Knowledge Engineering ◽  
Intelligent System ◽  
Content Management ◽  
Management Systems ◽  
Acquisition Process ◽  
Mathematical Properties ◽  
Process Metrics ◽  
Content Management Systems

Measuring the processes involved in knowledge engineering for designing and building an intelligent system has taken significant role. Out of the four basic processes involved in knowledge engineering, this paper deals with the knowledge acquisition process and the metrics necessary for measuring the process itself. Three metrics are proposed for the knowledge acquisition process based on the entailment procedures, its length and complexity, and the cohesion and coupling attributes of the collection of knowledge units. These three metrics are formalized based on the Briand’s mathematical properties for validating software metrics. These metrics are indicative in the way it gives an insight on the design and the development of a knowledgebase. In addition to these metrics, newer metrics can also be proposed for knowledge representation and knowledge sharing processes.

Conference interpreting and knowledge acquisition

Interpreting ◽  
2018 ◽  
Vol 20 (2) ◽  
pp. 204-231 ◽  
Author(s):  
Chia-chien Chang ◽  
Michelle Min-chia Wu ◽  
Tien-chun Gina Kuo
Keyword(s):  
Knowledge Acquisition ◽  
Target Audience ◽  
Language Usage ◽  
Specific Knowledge ◽  
Acquisition Process ◽  
Knowledge Domains ◽  
Domain Specific ◽  
Analytical Skills ◽  
Short Notice ◽  
Domain Specific Knowledge

Abstract This paper describes knowledge acquisition of professional conference interpreters in Taiwan when dealing with unfamiliar topics: the focus is on how the required knowledge is developed before, during and after a conference. We interviewed 10 Chinese-English interpreters, to find out about their preparation for such conferences and their approach to developing domain-specific knowledge. We first collected each interpreter’s five latest conference programs and used these to analyze the knowledge domains covered. We then based each interview on one conference agenda, considered representative by the interpreter, to examine the knowledge acquisition process from pre- to post-conference. The results show strategic preparation of unfamiliar topics: to facilitate comprehension and reformulation, interpreters make good use of conference documents and compile glossaries in which they organize the concepts and terminology specific to the conference. As they assimilate the language usage of the presenters and other participants during the conference, they use their analytical skills to manage any difficulties. Keeping in mind the aims of the event (e.g., commercial, scientific), as well as the profiles of the speakers and target audience, helps to optimize availability of relevant knowledge at short notice and continue updating it during the assignment.

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