9 research outputs found

    Conceptual knowledge acquisition in biomedicine: A methodological review

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    AbstractThe use of conceptual knowledge collections or structures within the biomedical domain is pervasive, spanning a variety of applications including controlled terminologies, semantic networks, ontologies, and database schemas. A number of theoretical constructs and practical methods or techniques support the development and evaluation of conceptual knowledge collections. This review will provide an overview of the current state of knowledge concerning conceptual knowledge acquisition, drawing from multiple contributing academic disciplines such as biomedicine, computer science, cognitive science, education, linguistics, semiotics, and psychology. In addition, multiple taxonomic approaches to the description and selection of conceptual knowledge acquisition and evaluation techniques will be proposed in order to partially address the apparent fragmentation of the current literature concerning this domain

    The semantic database model as a basis for an automated database design tool

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    Bibliography: p.257-80.The automatic database design system is a design aid for network database creation. It obtains a requirements specification from a user and generates a prototype database. This database is compatible with the Data Definition Language of DMS 1100, the database system on the Univac 1108 at the University of Cape Town. The user interface has been constructed in such a way that a computer-naive user can submit a description of his organisation to the system. Thus it constitutes a powerful database design tool, which should greatly alleviate the designer's tasks of communicating with users, and of creating an initial database definition. The requirements are formulated using the semantic database model, and semantic information in this model is incorporated into the database as integrity constraints. A relation scheme is also generated from the specification. As a result of this research, insight has been gained into the advantages and shortcomings of the semantic database model, and some principles for 'good' data models and database design methodologies have emerged

    Data and Language in Organizations: Epistemological Aspects of Management Support Systems

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    This book contributes to the literature on management decision support systems (DSS). DSS research is motivated by the observation that much of what managers do involves unstructured problem solving. For the reason, the structured, procedural models implemented in management information systems (MIS) have had little impact on actual managerial practice. Actually, the terms "decision" and "problem solving" over-simplify the image of managerial activity, if what is meant is choosing from a set of well-defined alternatives. Management also includes such aspects as reality testing, problem finding, scenario generation, and just plain muddling through. A broader conception of management cognition -- of which decision making is only a part -- is therefore adopted. The challenge to technology development is to support these unstructured managerial activities. The emphasis is to amplify managerial cognition and to improve decision effectiveness. However, to achieve this we must go beyond platitudes and come to a better understanding of what managers actually do. The activity of managers is almost entirely linguistic. Computers, as symbolic processors, ought to be an effective complement. However, a fundamental problem, stressed repeatedly throughout the book, is semantic change. The context of managers is always changing, whereas computational inference depends on fixed semantics. Herein Lies the basis for a theory of management support systems. The theory takes the form of an applied epistemology: how do managers know their world and detect its changes? Thus, while this book is oriented towards improving information technology, its attention is primarily to the content of management information and only secondarily to technology. Technological innovations abound. What is needed now is a better understanding of what these technologies are to do

    RELATIONSHIP ANALYSIS OF IMAGE DESCRIPTIONS: AN ONTOLOGICAL, CONTENT ANALYTIC APPROACH

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    The relationships humans express when describing images have powerful, but poorly understood, effects on how visual information is represented, structured, and processed in information systems. This study evaluates the benefits and difficulties of using content analysis and ontological analysis as predictors of relationship instances and types occurring in image descriptions. A random sample of 36 documented reference transactions from the administrative files of the Pittsburgh Photographic Library is analyzed in light of three describing contexts: image searcher, curator, and cataloger. Through the qualitative and quantitative assessment of image descriptions, the research leads to several key findings and contributions. The most important findings vindicate the claim that recognition, capture, and classification of relationship instances can be empirically grounded utilizing content analysis and ontological tools and methods. Evidence comes in successfully ascertaining and capturing in a Corpus the existence of 1,655 relationship instances. Further, the analysis finds evidence of relationship types and subtypes of relationships whose members share certain recognizable properties in common. The study also brings useful, new insights to the capture of background information surrounding events using situation-templates, introduces methods for formulating case relations and image attributes as binary predicates, and it offers a new, finer-grained definition of relationship. Contributions of this study include a corpus of relationship instances, an ontology of relationship types, and a methodological framework that provides significantly better results than earlier studies in the prediction of relationships (the architecture of which is depicted in Figure 22 on page 102). There are a number of ways this research could be extended and corroborated. First, event analysis ought to be tied to a system of semantic frame analysis. Second, test the content analysis form against other texts, which will result in elaboration of the core ontology of relationship types. Third, expand image description analysis beyond the current domain to include image description in visual ethnography, art history and criticism, and photography practices. Fourth, test how inference engines reason over relationships in knowledge-based environments. Finally, to aid in the analysis of the meanings of relationships, more work is needed in formalizing the ontological concepts used in image descriptions

    An Introduction to Database Systems

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    This textbook introduces the basic concepts of database systems. These concepts are presented through numerous examples in modeling and design. The material in this book is geared to an introductory course in database systems offered at the junior or senior level of Computer Science. It could also be used in a first year graduate course in database systems, focusing on a selection of the advanced topics in the latter chapters
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