Please read the abstract in the 00front part of this document / Dissertation (MA (Information Science))--University of Pretoria, 2006. / Information Science / unrestricted

Dissertation submitted in fulfillment of the requirements for the award of the degree Master of Library and Information Science of the University of Zululand, 2002. / Women constitute 51,6 % of the population of South Africa. It is widely known that in many households, especially blacks, men leave homes for urban areas in search for jobs, leaving women behind to maintain the entire household on their own. Despite significant role played by the women, they are faced with many problems as those experienced by women in other developing countries such as illiteracy, poverty, low social status and multiple roles of child care, farming and housework. The plight of women in rural parts of South Africa, hinder their access and exploitation of media, that can enable them to participate responsibly in nation building. The study explores the media needs of the rural women and how they are fulfilled in the areas of KwaZulu-Natal. Through a normative survey method and critical incident technique, 109 women were interviewed and questionnaires administered to 18 community leaders as well as observation for primary data. Evidently, there is need for suitable media where information on agriculture, health, education, business, legal, and career opportunities as well as self-help-related activities can be accessed. Such media should be available closer to home environment, in visual and sound formats, orally delivered by people closer to the community such as friends, relatives and neighbours as well as community leaders. Methods of information transmission should be overwhelmingly oral that includes greater use of radio transmission. Largely, the findings are consistent with similar studies conducted elsewhere in the developing countries. Conference presentation and publication in a peer-referred journal is planned in 2002. / University of Zululand

This thesis describes WebDoc, an automated system that classifies Web documents according to the Library of Congress classification system. This work is an extension of an early version of the system that successfully generated indexes for journal articles. The unique features of Web documents, as well as how they will affect the design of a classification system, are discussed. We argue that full-text analysis of Web documents is inevitable, and contextual information must be used to assist the classification. The architecture of the WebDoc system is presented. We performed experiments on it with and without the assistance of contextual information. The results show that contextual information improved the system?s performance significantly.

Civil unrest (protests, strikes, and ``occupy'' events) is a common occurrence in both democracies and authoritarian regimes. The study of civil unrest is a key topic for political scientists as it helps capture an important mechanism by which citizenry express themselves. In countries where civil unrest is lawful, qualitative analysis has revealed that more than 75% of the protests are planned, organized, and/or announced in advance; therefore detecting future time mentions in relevant news and social media is a simple way to develop a protest forecasting system. We develop such a system in this thesis, using a combination of key phrase learning to identify what to look for, probabilistic soft logic to reason about location occurrences in extracted results, and time normalization to resolve future tense mentions. We illustrate the application of our system to 10 countries in Latin America, viz. Argentina, Brazil, Chile, Colombia, Ecuador, El Salvador, Mexico, Paraguay, Uruguay, and Venezuela. Results demonstrate our successes in capturing significant societal unrest in these countries with an average lead time of 4.08 days. We also study the selective superiorities of news media versus social media (Twitter, Facebook) to identify relevant tradeoffs. / Master of Science

Univ., Diss.--Tübingen, 2005.

Diss. Univ. Konstanz, 1999.

Zugl.: Münster (Westfalen), Universiẗat, Diss., 2000.

Module extraction is an important reasoning task, aiding in the design, reuse and maintenance of ontologies. Reasoning services such as subsumption testing and MinA extraction have been shown to bene t from module extraction methods. Though various syntactic traversal-based module extraction algorithms exist for extracting modules, many only consider the subsumee of a subsumption statement as a selection criterion for reducing the axioms in the module. In this dissertation we extend the bottom-up reachability-based module extraction heuristic for the inexpressive Description Logic EL, by introducing a top-down version of the heuristic which utilises the subsumer of a subsumption statement as a selection criterion to minimize the number of axioms in a module. Then a combined bidirectional heuristic is introduced which uses both operands of a subsumption statement in order to extract very small modules. We then investigate the relationship between MinA extraction and bidirectional reachabilitybased module extraction. We provide empirical evidence that bidirectional reachability-based module extraction for subsumption entailments in EL provides a signi cant reduction in the size of modules for almost no additional costs in the running time of the original algorithms. / Computer Science / M. Sc. (Computer Science)

Case-Based Reasoning (CBR) is a technique in Artificial Intelligence where a new problem is solved by making use of the solution to a similar past problem situation. People naturally solve problems in this way, without even thinking about it. For example, an occupational therapist (OT) that assesses the needs of a new disabled person may be reminded of a previous person in terms of their disabilities. He may or may not decide to recommend the same devices based on the outcome of an earlier (disabled) person. Case-based reasoning makes use of a collection of past problem-solving experiences thus enabling users to exploit the information of others’ successes and failures to solve their own problem(s). This project has developed a CBR tool to assist in matching SmartHouse technology to the needs of the elderly and people with disabilities. The tool makes suggestions of SmartHouse devices that could assist with given impairments. SmartHouse past problem-solving textual reports have been used to obtain knowledge for the CBR system. Creating a case-based reasoning system from textual sources is challenging because it requires that the text be interpreted in a meaningful way in order to create cases that are effective in problem-solving and to be able to reasonably interpret queries. Effective case retrieval and query interpretation is only possible if a domain-specific conceptual model is available and if the different meanings that a word can take can be recognised in the text. Approaches based on methods in information retrieval require large amounts of data and typically result in knowledge-poor representations. The costs become prohibitive if an expert is engaged to manually craft cases or hand tag documents for learning. Furthermore, hierarchically structured case representations are preferred to flat-structured ones for problem-solving because they allow for comparison at different levels of specificity thus resulting in more effective retrieval than flat structured cases. This project has developed SmartCAT-T, a tool that creates knowledge-rich hierarchically structured cases from semi-structured textual reports. SmartCAT-T highlights important phrases in the textual SmartHouse problem-solving reports and uses the phrases to create a conceptual model of the domain. The model then becomes a standard structure onto which each semi-structured SmartHouse report is mapped in order to obtain the correspondingly structured case. SmartCAT-T also relies on an unsupervised methodology that recognises word synonyms in text. The methodology is used to create a uniform vocabulary for the textual reports and the resulting harmonised text is used to create the standard conceptual model of the domain. The technique is also employed in query interpretation during problem solving. SmartCAT-T does not require large sets of tagged data for learning, and the concepts in the conceptual model are interpretable, allowing for expert refinement of knowledge. Evaluation results show that the created cases contain knowledge that is useful for problem solving. An improvement in results is also observed when the text and queries are harmonised. A further evaluation highlights a high potential for the techniques developed in this research to be useful in domains other than SmartHouse. All this has been implemented in the Smarter case-based reasoning system.

This paper reports on an exploratory study that investigates the relationship between participants' learning style preferences and their perceptions of a professional workshop on collaboration and technology to support collaboration. The Learning Preference Scale-Students (LPSS) (Owens & Barnes, 1992) was administered to identify participants' learning style preferences as cooperative, competitive and/or individualized. Using cluster analysis two groups, or categories, of learning style preferences among the participants emerged. Group 1 showed a strong preference for the cooperative learning style, and Group 2 showed a strong preference for competitive and cooperative learning styles. Group 1 rated the workshop more positively than Group 2. However, Group 2 reported a larger increase in self-efficacy compared to those in Group 1 (18.9% vs. 6.0%). Both groups provided different suggestions regarding the content of the workshop. Group 1 suggested adding more discussions and group exercises, whereas Group 2 suggested adding explicit theory or rules to govern behavior. These findings indicate that learning styles should be considered as a potential variable that influences learning outcomes and preferences.