A Domain-Independent Otology-based Approach to Representation of Courseware Knowledge

This paper proposes an ontology-based approach to representation of courseware knowledge in different domains. The focus is on a three-level semantic graph, modeling respectively the course as a whole, its structure, and domain contents itself. The authors plan to use this representation for flexibie e- learning and generation of different study plans for the learners.

Knowledge-Based Approach to Document Analysis

The paper presents an approach to extraction of facts from texts of documents. This approach is based on using knowledge about the subject domain, specialized dictionary and the schemes of facts that describe fact structures taking into consideration both semantic and syntactic compatibility of elements of facts. Actually extracted facts combine into one structure the dictionary lexical objects found in the text and match them against concepts of subject domain ontology.

Ontological Approach to Domain Knowledge Representation for Information Retrieval in Multiagent Systems

An ontological representation of buyer interests’ knowledge in process of e-commerce is proposed to use. It makes it more efficient to make a search of the most appropriate sellers via multiagent systems. An algorithm of a comparison of buyer ontology with one of e-shops (the taxonomies) and an e-commerce multiagent system are realised using ontology of information retrieval in distributed environment.

Unravelling the evolution of autumn colours: an interdisciplinary approach.

Leaf colour change is commonly observed in temperate deciduous forests in autumn. This is not simply a side effect of leaf senescence, and, in the past decade, several hypotheses have emerged to explain the evolution of autumn colours. Yet a lack of crosstalk between plant physiologists and evolutionary ecologists has resulted in slow progress, and so the adaptive value of this colour change remains a mystery. Here we provide an interdisciplinary summary of the current body of knowledge on autumn colours, and discuss unresolved issues and future avenues of research that might help reveal the evolutionary meaning of this spectacle of nature.

A socio-technological approach to sharing knowledge across disciplines

Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal.

A socio-technological approach to sharing knowledge across disciplines

Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal.

A Knowledge Management Process-Based Approach to Support Corporate Crisis Management

In the crisis-prone and complex contemporary business environment, modern organisations and their supply chains, large and small, are challenged by crises more than ever. Knowledge management has been acknowledged as an important discipline able to support the management of complexity in times of crisis. However, the role of effective knowledge retrieval and sharing in the process of crisis prevention, management and survival has been relatively underexplored. In this paper, it is argued that organisational crises create additional challenges for knowledge management, mainly because complex, polymorphic and both structured and unstructured knowledge must be efficiently harnessed, processed and disseminated to the appropriate internal and external supply chain actors, under specific time constraints. In this perspective, a process-based approach is proposed to address the knowledge management needs of organisations during a crisis and to help management in establishing the necessary risk avoidance and recovery mechanisms. Finally, the proposed methodological approach is applied in a knowledge- intensive Greek small and medium enterprise from the pharmaceutical industry, producing empirical results, insights on knowledge pathologies during crises and relevant evaluations.

Tacit knowledge sharing: the determination of a methodological approach to explore the intangible.

Introduction. Research design should take into account both (a) the specific nature of the object under scrutiny, and (b) approaches to its study in the past. This is to ensure that informed decisions are made regarding research design in future empirical studies. Here these factors are taken into account with reference to methodological choice for a doctoral study on tacit knowledge sharing, and the extent to tacit knowledge sharing may be facilitated by online tools. The larger study responds to calls for the two domains of knowledge management and human information behaviour to be considered together in terms of their research approaches and theory development. Method. Relevant literature – both domain-specific (knowledge management) and general (research methods in social science) - was identified and analysed to identify the most appropriate approaches for an empirical study of tacit knowledge sharing. Analysis. The analysis shows that there are a number of challenges associated with studying an intangible entity such as tacit knowledge. Quantitative, qualitative and mixed methods have been adopted in prior work on this theme, each with their own strengths and weaknesses. Results. The analysis has informed a decision to adopt a research approach that deploys mixed methods for an inductive case study to extend knowledge of the influence of online tools on tacit knowledge sharing. Conclusion. This work intends to open the debate on methodological choice and routes to implementation for studies that are subject to practical constraints imposed by the context in which they are situated.

Public firm sponsorship of open source software: A knowledge-based approach to open innovation

As an emerging innovation paradigm gaining momentum in recent years, the open innovation paradigm is calling for greater theoretical depth and more empirical research. This dissertation proposes that open innovation in the context of open source software sponsorship may be viewed as knowledge strategies of the firm. Hence, this dissertation examines the performance determinants of open innovation through the lens of knowledge-based perspectives. Using event study and regression methodologies, this dissertation found that these open source software sponsorship events can indeed boost the stock market performance of US public firms. In addition, both the knowledge capabilities of the firms and the knowledge profiles of the open source projects they sponsor matter for performance. In terms of firm knowledge capabilities, internet service firms perform better than other firms owing to their advantageous complementary capabilities. Also, strong knowledge exploitation capabilities of the firm are positively associated with performance. In terms of the knowledge profile of sponsored projects, platform projects perform better than component projects. Also, community-originated projects outperform firm-originated projects. Finally, based on these findings, this dissertation discussed the important theoretical implications for the strategic tradeoff between knowledge protection and sharing.

A Brazilian Original Pedagogical Approach To The Teaching Of Neurology.

The two-arm Clinical Decisions/Diagnostic Workshop (CD/DW) approach to undergraduate medical education has been successfully used in Brazil. Present the CD/DW approach to the teaching of stroke, with the results of its pre-experimental application and of a comparative study with the traditional lecture-case discussion approach. Application of two questionnaires (opinion and Knowledge-Attitudes-Perceptions-KAP) to investigate the non-inferiority of the CD/DW approach. The method was well accepted by teachers and students alike, the main drawback being the necessarily long time for its completion by the students, a feature that may better cater for different educational needs. The comparative test showed the CD/DW approach to lead to slightly higher cognitive acquisition as opposed to the traditional method, clearly showing its non-inferiority status. The CD/DW approach seems to be another option for teaching neurology in undergraduate medical education, with the bonus of respecting each learner`s time.

A simulation-based evolutionary multiobjective approach to manufacturing cell formation

The purpose of this paper is to propose a multiobjective optimization approach for solving the manufacturing cell formation problem, explicitly considering the performance of this said manufacturing system. Cells are formed so as to simultaneously minimize three conflicting objectives, namely, the level of the work-in-process, the intercell moves and the total machinery investment. A genetic algorithm performs a search in the design space, in order to approximate to the Pareto optimal set. The values of the objectives for each candidate solution in a population are assigned by running a discrete-event simulation, in which the model is automatically generated according to the number of machines and their distribution among cells implied by a particular solution. The potential of this approach is evaluated via its application to an illustrative example, and a case from the relevant literature. The obtained results are analyzed and reviewed. Therefore, it is concluded that this approach is capable of generating a set of alternative manufacturing cell configurations considering the optimization of multiple performance measures, greatly improving the decision making process involved in planning and designing cellular systems. (C) 2010 Elsevier Ltd. All rights reserved.

Multilabeled classification approach to find a plaint source for terpenoids

Recently, we have built a classification model that is capable of assigning a given sesquiterpene lactone (STL) into exactly one tribe of the plant family Asteraceae from which the STL has been isolated. Although many plant species are able to biosynthesize a set of peculiar compounds, the occurrence of the same secondary metabolites in more than one tribe of Asteraceae is frequent. Building on our previous work, in this paper, we explore the possibility of assigning an STL to more than one tribe (class) simultaneously. When an object may belong to more than one class simultaneously, it is called multilabeled. In this work, we present a general overview of the techniques available to examine multilabeled data. The problem of evaluating the performance of a multilabeled classifier is discussed. Two particular multilabeled classification methods-cross-training with support vector machines (ct-SVM) and multilabeled k-nearest neighbors (M-L-kNN)were applied to the classification of the STLs into seven tribes from the plant family Asteraceae. The results are compared to a single-label classification and are analyzed from a chemotaxonomic point of view. The multilabeled approach allowed us to (1) model the reality as closely as possible, (2) improve our understanding of the relationship between the secondary metabolite profiles of different Asteraceae tribes, and (3) significantly decrease the number of plant sources to be considered for finding a certain STL. The presented classification models are useful for the targeted collection of plants with the objective of finding plant sources of natural compounds that are biologically active or possess other specific properties of interest.

An agent-based approach to modelling driver route choice behaviour under the influence of real-time information

This paper presents an agent-based approach to modelling individual driver behaviour under the influence of real-time traffic information. The driver behaviour models developed in this study are based on a behavioural survey of drivers which was conducted on a congested commuting corridor in Brisbane, Australia. Commuters' responses to travel information were analysed and a number of discrete choice models were developed to determine the factors influencing drivers' behaviour and their propensity to change route and adjust travel patterns. Based on the results obtained from the behavioural survey, the agent behaviour parameters which define driver characteristics, knowledge and preferences were identified and their values determined. A case study implementing a simple agent-based route choice decision model within a microscopic traffic simulation tool is also presented. Driver-vehicle units (DVUs) were modelled as autonomous software components that can each be assigned a set of goals to achieve and a database of knowledge comprising certain beliefs, intentions and preferences concerning the driving task. Each DVU provided route choice decision-making capabilities, based on perception of its environment, that were similar to the described intentions of the driver it represented. The case study clearly demonstrated the feasibility of the approach and the potential to develop more complex driver behavioural dynamics based on the belief-desire-intention agent architecture. (C) 2002 Elsevier Science Ltd. All rights reserved.

Molecular epidemiology: A multidisciplinary approach to understanding parasitic zoonoses

Sound application of molecular epidemiological principles requires working knowledge of both molecular biological and epidemiological methods. Molecular tools have become an increasingly important part of studying the epidemiology of infectious agents. Molecular tools have allowed the aetiological agent within a population to be diagnosed with a greater degree of efficiency and accuracy than conventional diagnostic tools. They have increased the understanding of the pathogenicity, virulence, and host-parasite relationships of the aetiological agent, provided information on the genetic structure and taxonomy of the parasite and allowed the zoonotic potential of previously unidentified agents to be determined. This review describes the concept of epidemiology and proper study design, describes the array of currently available molecular biological tools and provides examples of studies that have integrated both disciplines to successfully unravel zoonotic relationships that would otherwise be impossible utilising conventional diagnostic tools. The current limitations of applying these tools, including cautions that need to be addressed during their application are also discussed.(c) 2005 Australian Society for Parasitology Inc. Published by Elsevier Ltd. All rights reserved.

A Bayesian approach to fuzzy hypotheses testing for the estimation of optimal age for vaccination against measles

Fuzzy Bayesian tests were performed to evaluate whether the mother`s seroprevalence and children`s seroconversion to measles vaccine could be considered as ""high"" or ""low"". The results of the tests were aggregated into a fuzzy rule-based model structure, which would allow an expert to influence the model results. The linguistic model was developed considering four input variables. As the model output, we obtain the recommended age-specific vaccine coverage. The inputs of the fuzzy rules are fuzzy sets and the outputs are constant functions, performing the simplest Takagi-Sugeno-Kang model. This fuzzy approach is compared to a classical one, where the classical Bayes test was performed. Although the fuzzy and classical performances were similar, the fuzzy approach was more detailed and revealed important differences. In addition to taking into account subjective information in the form of fuzzy hypotheses it can be intuitively grasped by the decision maker. Finally, we show that the Bayesian test of fuzzy hypotheses is an interesting approach from the theoretical point of view, in the sense that it combines two complementary areas of investigation, normally seen as competitive. (C) 2007 IMACS. Published by Elsevier B.V. All rights reserved.