Relevance, Rhetoric, and Argumentation: A Cross-Disciplinary Inquiry into Patterns of Thinking and Information Structuring

This dissertation research is a multidisciplinary inquiry into topicality, involving an in-depth examination of literatures and empirical data and an inductive development of a faceted typology (containing 227 fine-grained topical relevance relationships and 33 types of presentation relationship). This inquiry investigates a large variety of topical connections beyond topic matching, renders a closer look into the structure of a topic, achieves an enriched understanding of topicality and relevance, and induces a cohesive topic-oriented information architecture that is meaningful across topics and domains. The findings from the analysis contribute to the foundation work of information organization, intellectual access / information retrieval, and knowledge discovery. Using qualitative content analysis, the inquiry focuses on meaning and deep structure: Phase 1 : develop a unified theory-grounded typology of topical relevance relationships through close reading of literature and synthesis of thinking from communication, rhetoric, cognitive psychology, education, information science, argumentation, logic, law, medicine, and art history; Phase 2 : in-depth qualitative analysis of empirical relevance datasets in oral history, clinical question answering, and art image tagging, to examine manifestations of the theory-grounded typology in various contexts and to further refine the typology; the three relevance datasets were used for analysis to achieve variation in form, domain, and context. The typology of topical relevance relationships is structured with three major facets: Functional role of a piece of information plays in the overall structure of a topic or an argument; Mode of reasoning: How information contributes to the user's reasoning about a topic; Semantic relationship: How information connects to a topic semantically. This inquiry demonstrated that topical relevance with its close linkage to thinking and reasoning is central to many disciplines. The multidisciplinary approach allows synthesis and examination from new angles, leading to an integrated scheme of relevance relationships or a system of thinking that informs each individual discipline. The scheme resolving from the synthesis can be used to improve text and image understanding, knowledge organization and retrieval, reasoning, argumentation, and thinking in general, by people and machines

An evaluation of the challenges of Multilingualism in Data Warehouse development

In this paper we discuss Business Intelligence and define what is meant by support for Multilingualism in a Business Intelligence reporting context. We identify support for Multilingualism as a challenging issue which has implications for data warehouse design and reporting performance. Data warehouses are a core component of most Business Intelligence systems and the star schema is the approach most widely used to develop data warehouses and dimensional Data Marts. We discuss the way in which Multilingualism can be supported in the Star Schema and identify that current approaches have serious limitations which include data redundancy and data manipulation, performance and maintenance issues. We propose a new approach to enable the optimal application of multilingualism in Business Intelligence. The proposed approach was found to produce satisfactory results when used in a proof-of-concept environment. Future work will include testing the approach in an enterprise environmen