Ontologias de representação de conhecimento são modelos que agrupam e definem construtos de modelagem (tais como conceito, classe, frame). Uma ontologia temporal é uma especificação de uma conceitualização do domínio acrescida dos aspectos de tempo sobre os objetos do domínio. No caso de esta ontologia tratar somente de aspectos temporais ligados a eventos, ela é chamada ontologia de eventos. Este trabalho propõe uma extensão de uma ontologia de representação de conhecimento (RC) com construtos temporais, ou seja, estender um modelo de conceitualização de domínios com os construtos necessários para se representar eventos. A definição original de uma ontologia de RC foi estendida neste trabalho com os seguintes construtos: (i) o construto evento (que define um evento como um acontecimento que pode alterar objetos do domínio) e (ii) o construto relação-temporal (que define uma relação de tempo entre os eventos). O objetivo deste trabalho é, baseando-se nos novos construtos propostos na ontologia de RC, criar um modelo de conhecimento específico para um domínio, o qual suporta a representação das seguintes primitivas: características do domínio, eventos geradores das características do domínio, associações entre características do domínio, relações temporais entre eventos. Além disso, o modelo tem o objetivo de suportar métodos de raciocínio para inferir a seqüência em que as características do domínio foram geradas pelos eventos, ou seja, inferir a ordem dos eventos. O domínio de aplicação deste trabalho é a petrografia sedimentar. Esse domínio apresenta diversas tarefas de interpretação que exigem conhecimento especializado para sua solução. Dentre estas tarefas, o foco de estudo deste trabalho é a interpretação de seqüência diagenética, a qual procura identificar a ordem em que os processos diagenéticos atuaram sobre os constituintes das rochas sedimentares. Para compreender e coletar o conhecimento necessário para a modelagem dessa tarefa, as principais técnicas de aquisição de conhecimento para sistemas baseados em conhecimento foram aplicadas em sessões com um especialista em petrografia sedimentar, gerando os principais objetos do domínio que precisavam ser modelados para se atingir o objetivo da ordenação de eventos. Esses objetos foram representados como um modelo de conhecimento específico para o domínio da petrografia, aplicando os construtos de eventos e relação temporal. Esse modelo foi validado através da implementação de um módulo de inferência no sistema PetroGrapher. A validação foi efetuada comparando as respostas do algoritmo com as interpretações do especialista, e foi possível notar que apesar de a apresentação gráfica da seqüência de eventos ainda ser diferente da original do especialista, o algoritmo efetua a ordenação de eventos corretamente. / O domínio de aplicação deste trabalho é a petrografia sedimentar. Esse domínio apresenta diversas tarefas de interpretação que exigem conhecimento especializado para sua solução. Dentre estas tarefas, o foco de estudo deste trabalho é a interpretação de seqüência diagenética, a qual procura identificar a ordem em que os processos diagenéticos atuaram sobre os constituintes das rochas sedimentares. Para compreender e coletar o conhecimento necessário para a modelagem dessa tarefa, as principais técnicas de aquisição de conhecimento para sistemas baseados em conhecimento foram aplicadas em sessões com um especialista em petrografia sedimentar, gerando os principais objetos do domínio que precisavam ser modelados para se atingir o objetivo da ordenação de eventos. Esses objetos foram representados como um modelo de conhecimento específico para o domínio da petrografia, aplicando os construtos de eventos e relação temporal. Esse modelo foi validado através da implementação de um módulo de inferência no sistema PetroGrapher. A validação foi efetuada comparando as respostas do algoritmo com as interpretações do especialista, e foi possível notar que apesar de a apresentação gráfica da seqüência de eventos ainda ser diferente da original do especialista, o algoritmo efetua a ordenação de eventos corretamente. The application domain of this work is the sedimentary petrography. This domain presents several interpretation tasks that demand specialized knowledge for their solution. Among these tasks, the focus of this work is the diagenetic sequence interpretation, which aims to identify the order in that diagenetic processes occurred over the constituents of sedimentary rocks. In order to understand and collect the necessary knowledge for modeling this task, the main techniques of knowledge acquisition for knowledge-based systems were applied to an expert in sedimentary petrography. These sessions of knowledge acquisition revealed the domain objects that needed to be modeled to reach the objective of the ordering of events. Those objects were represented in a knowledge model for the petrography domain, which applied the proposed constructs (events and time relation). That model was validated by the implementation of an inference module in the PetroGrapher system. The validation proceeded by comparing the outputs of the algorithm with the expert's interpretations, and it was possible to notice that despite the graphic presentation of the sequence of events being different from what the expert produces as a sequence of events, the algorithm is able to order the events correctly.

M.Ing. ( Electrical & Electronic Engineering Science) / Economic modelling tools have gained popularity in recent years due to the increasing need for greater knowledge to assist policy makers and economists. A number of computational intelligence approaches have been proposed for economic modelling. Most of these approaches focus on the accuracy of prediction and not much research has been allocated to investigate the interpretability of the decisions derived from these systems. This work proposes the use of computational intelligence techniques (Rough set theory (RST) and the Multi-layer perceptron (MLP) model) to model the South African economy. RST is a rule-based technique suitable for analysing vague, uncertain and imprecise data. RST extracts rules from the data to model the system. These rules are used for prediction and interpreting the decision process. The lesser the number of rules, the easier it is to interpret the model. The performance of the RST is dependent on the discretization technique employed. An equal frequency bin (EFB), Boolean reasoning (BR), entropy partition (EP) and the Naïve algorithm (NA) are used to develop an RST model. The model trained using EFB data performs better than the models trained using BR and EP. RST was used to model South Africa’s financial sector. Here, accuracy of 86.8%, 57.7%, 64.5% and 43% were achieved for EFB, BR, EP and NA respectively. This work also proposes an ensemble of rough set theory and the multi-layer perceptron model to model the South African economy wherein, a prediction of the direction of the gross domestic product is presented. This work also proposes the use of an auto-associative Neural Network to impute missing economic data. The auto-associative neural network imputed the ten variables or attributes that were used in the prediction model. These variables were: Construction contractors rating lack of skilled labour as constraint, Tertiary economic sector contribution to GDP, Income velocity of circulation of money, Total manufacturing production volume, Manufacturing firms rating lack of skilled labour as constraint, Total asset value of banking industry, Nominal unit labour cost, Total mass of Platinum Group Metals (PGMs) mined, Total revenue from sale of PGMs and the Gross Domestic Expenditure (GDE). The level of imputation accuracy achieved varied with the attribute. The accuracy ranged from 85.9% to 98.7%.

The Semantic Web is a vision to create a “web of knowledge”; an extension of the Web as we know it which will create an information space which will be usable by machines in very rich ways. The technologies which make up the Semantic Web allow machines to reason across information gathered from the Web, presenting only relevant results and inferences to the user. Users of the Web in its current form assess the credibility of the information they gather in a number of different ways. If processing happens without the user being able to check the source and credibility of each piece of information used in the processing, the user must be able to trust that the machine has used trustworthy information at each step of the processing. The machine should therefore be able to automatically assess the credibility of each piece of information it gathers from the Web. A case study on advanced checks for website credibility is presented, and the site presented in the case presented is found to be credible, despite failing many of the checks which are presented. A website with a backend based on RDF technologies is constructed. A better understanding of RDF technologies and good knowledge of the RAP and Redland RDF application frameworks is gained. The second aim of constructing the website was to gather information to be used for testing various trust metrics. The website did not gain widespread support, and therefore not enough data was gathered for this. Techniques for presenting RDF data to users were also developed during website development, and these are discussed. Experiences in gathering RDF data are presented next. A scutter was successfully developed, and the data smushed to create a database where uniquely identifiable objects were linked, even where gathered from different sources. Finally, the use of digital signature as a means of linking an author and content produced by that author is presented. RDF/XML canonicalisation is discussed in the provision of ideal cryptographic checking of RDF graphs, rather than simply checking at the document level. The notion of canonicalisation on the semantic, structural and syntactic levels is proposed. A combination of an existing canonicalisation algorithm and a restricted RDF/XML dialect is presented as a solution to the RDF/XML canonicalisation problem. We conclude that a trusted Semantic Web is possible, with buy in from publishing and consuming parties.

Orientador: Marcio Luiz de Andrade Netto / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica / Made available in DSpace on 2018-07-13T23:17:40Z (GMT). No. of bitstreams: 1 Schiavini_MarcosMelo_M.pdf: 13404383 bytes, checksum: fe85c025b2fd3ced855421c365c05348 (MD5) Previous issue date: 1991 / Resumo: A tese apresenta uma contribuição para agilizar e organizar o processo de aquisição de conhecimento necessário ao desenvolvimento de sistemas Especialistas. Para tanto é descrita uma ferramenta computacional de auxilio ao processo de aquisição e engenharia de conhecimento - CAKE - que emprega um modelo do domínio durante sua interação com o especialista. O modelo é elaborado e representado com o auxílio do KADS, uma metodologia de construção de sistemas baseados em conhecimento [WIELINGA 89]. Com esse trabalho visamos obter uma ferramenta de aquisição de conhecimento que não apenas apresente as vantagens de empregar um modelo como também não tenha seu uso limitado a apenas um domínio particular. Para tanto concebemos uma ferramenta que deixa explícito o modelo utilizado para guiar o processo de aquisição de conhecimento. O engenheiro do conhecimento pode alterar a ferramenta para adequá-la às suas necessidades / Abstract: The theses presents a contribution to facilitate and to organize the knowledge acquisition process necessary in the development of Expert Systems. A computer aided knowledge acquisition and engineering tool - CAKE -that employs a domain model in its interaction with the expert is proposed. The model is constructed and represented with the help of KADS, a methodology to construct knowledge based systems [WIELINGA 89]. It is intended, with this work, to obtain a knowledge acquisition tool that not only has the advantages of using a model, but also does not have its applicability limited to a particular domain. For this purpose, we have conceived a tool that leaves explicit the model used in guiding the knowledge acquisition processo The knowledge engineer is able to modify the tool to make the necessary adaptations for his further needs / Mestrado / Mestre em Engenharia Elétrica

A model is designed for the restructuring of knowledge. By way of suitably designed ontologies knowledge can be analysed to facilitate the creation of new knowledge and to render the knowledge suitable for reuse and for linkage to word-wide ontologies. / SUMMARY The need for this research emanated from the requirement for learning and adaptation in the fast-changing world we live in today. The changing world goes along with developments in communication means, whereby information becomes more accessible and sophisticated daily. A vast number of resources is available and accessible, distributing an enormous amount of information. The need is to turn these vast amounts of information into usable knowledge for use by an engineer in practice. Restructuring of knowledge is one way of approaching this need and is addressed in this study. This process can be facilitated by experienced persons who know what knowledge is needed in practice. There is a decline in the numbers of experienced civil engineers, leaving a gap between the supply and demand for suitably qualified and experienced civil engineers. The objective of this study is to meet the need for the restructuring of knowledge by the design a model (referred to as a logic base in this study) for the acquisition, reuse and the creation of engineering knowledge in a civil engineering environment. The main research question posed in this study is as follows: What are the key characteristics of a model (termed a “logic base” in this study) for the acquisition, reuse and the creation of knowledge in a civil engineering environment? This research commences with a set of research questions, followed by a literature review. Consideration is given to theories of knowledge, various methods of knowledge creation and knowledge acquisition. Several problem-solving techniques are reviewed. The structuring and architecture of knowledge and ontologies are researched and the role of systems engineering is studied. Various research methods are investigated and it is shown that case study research is the most suitable for the development of ontologies in civil engineering. The ontology of the logic base is therefore based on typical topics of case studies. Concept maps are employed to structure knowledge. This is done by defining appropriate concepts and classifying these into several ontological levels. The relationships among concepts and other influencing domains are studied. Knowledge of these relationships enables the application of several problem-solving techniques that enhance and stimulate the creation of knowledge. A logic base is designed containing three modules, namely an input module whereby concept maps are used to capture and structure knowledge entities. The second module consists of an analysis module where problem-solving can be done. The third module contains the output of work and processes where engineering knowledge can be documented for reuse. The contribution of this research lies in the design of an application in knowledge management in the field of civil engineering. Integration is done of ontologies, knowledge theories, knowledge acquisition and knowledge creation through problem-solving techniques. Knowledge is structured that can be linked to other external civil engineering taxonomies and ontologies. This enhancement of knowledge makes knowledge explicit and renders it suitable for reuse. When engineers are equipped in the use of the logic base, problems can be addressed in a holistic way and the underlying thought processes can be documented. This may be of great value to inexperienced engineers and for the preservation of valuable knowledge. Some case studies are analysed to demonstrate the functioning of the model. / Thesis (DPhil) University of Pretoria 2018. / Information Science / DPhil / Unrestricted

When medical researchers test a new treatment procedure, they recruit patients with appropriate health problems and medical histories. An experiment with a new procedure is called a clinical trial. The selection of patients for clinical trials has traditionally been a labor-intensive task, which involves matching of medical records with a list of eligibility criteria. A recent project at the University of South Florida has been aimed at the automation of this task. The project has involved the development of an expert system that selects matching clinical trials for each patient. If a patient's data are not sufficient for choosing a trial, the system suggests additional medical tests. We report the work on the representation and entry of the related selection criteria and medical tests. We first explain the structureof the system's knowledge base, which describes clinical trials and criteria for selecting patients. We then present an interface that enables a clinician to add new trials and selection criteria without the help of a programmer. Experiments show that the addition of a new clinical trial takes ten to twenty minutes, and that novice users learn the full functionality of the interface in about an hour.

This article is about technology search as an example of a knowledge acquisition task in industry. Technology search is about finding technology related information in structured as well as unstructured sources. This information is needed to support optimal decision making in business processes. There are new opptortunities for technology search and challenges that need to be addressed. This article outlines some of these challenges and presents two concepts to address them in a search system.

If the author of the published paper digitizes such paper and releases it to third parties using digital media such as computer networks or CD-ROMs, the volume, number, and pages of the Journal of Natural Language Processing of the publication must be indicated in a clear manner for all viewers. / 京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第19850号 / 情博第601号 / 新制||情||105(附属図書館) / 32886 / 京都大学大学院情報学研究科知能情報学専攻 / (主査)准教授 河原 大輔, 教授 黒橋 禎夫, 教授 河原 達也 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

High Fidelity Simulation is a teaching strategy that is becoming well-entrenched in the world of nursing education and is rapidly expanding due to the challenges and demands of the health care environment. The problem addressed in this study is the conflicting research results regarding the effectiveness of HFS for students’ knowledge acquisition after participating in simulation exercises. Specifically this researcher determined the effects of a formatted simulation scenario on knowledge acquisition among nursing students and the students’ satisfaction and selfconfidence with the simulation learning activity. Kolb’s Experiential Learning Theory (1984) provided the framework for this study. This study used a quantitative quasi-experimental design, specifically, the Solomon Four Research Design with 43 first semester senior nursing students enrolled at a baccalaureate nursing program at a state university in the southeastern United States. The results of the study found that there was not a statistically significant difference between the experimental group (E1) who received HFS (z = -1.47, p = 0.143) in cognitive gains when compared to the students who did not receive the intervention of HFS (C1) (z = -1.78, p = 0.75). The students’ overall perception of HFS was very positive and the simulation activity increased their self-reported level of self-confidence. The results of this study imply that simulation should not be used with the exclusive goal to increase knowledge but rather for students to increase their confidence and to demonstrate their ability to care for a patient at the bedside. It is our duty as nurse educators to systematically evaluate new teaching efforts such as simulation to determine the effectiveness of this remarkable but expensive technology to ensure that we are providing the best learning opportunities possible for our nursing students.

The purpose of this study was to examine the relationship between learning styles and student performance on a pre and post test, using an online case study, while also documenting their reactions to the case study. The case studies used in this research contained different storylines that showed multiple perspectives of case scenarios, giving students more choices to see what may happen in real school situations. Working with undergraduate students (N = 138) from the College of Education at a southeastern university, the researcher examined how students learned and responded to an online case study relative to their learning styles. Kolb's learning style inventory and a learner feedback survey questionnaire were administered respectively before and after the case study. Scores on Kolb's learning style inventory were used to classify the students' learning style preferences. A paired samples t-test was used to analyze the learners' knowledge test scores before and after the case study. The data revealed that the mean of students' post-test scores was significantly higher than the mean of their pre-test scores. Using descriptive methods, students' responses to the feedback questionnaire were analyzed. There was no difference shown between students with different learning style preferences, their overall reactions to the case study, and their reactions to certain elements (e.g., the content map, the assistants, and the navigation) included in the case study. Overall, most students' reactions to the case study were positive. Open-ended questions in the feedback questionnaire were analyzed and three assertions were generated. Of the optional features included within the case study, eighty two percent of students used the practice quizzes to self-check whether they understood the concepts and content covered in the cases. Students' post-test scores were congruent with their reactions to the online case study (with higher scoring students expressing more positive responses); and students' preferences regarding the use of online cases for study emerged in patterns relative to their career background. The study results showed that case studies can be used effectively in teacher education programs, while many learners (74%) favored using the case study and developed positive reactions through their case study experiences.