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Please read the abstract in the section 00front of this document / Thesis (DPhil (Information Science))--University of Pretoria, 2005. / Information Science / unrestricted

Thesis (Ph. D.)--University of Illinois at Urbana-Champaign, 1994. / eContent provider-neutral record in process. Description based on print version record.

Objective: This research explored how time-to-event dental articles were indexed and reported, and sought solutions to help improve the reporting and identification of these articles, so that they could be more easily found and used to inform practice and research. Methods: Articles reporting time-to-event dental outcomes in humans were identified from the 50 dental journals with the highest impact factor for 2008. These were handsearched, identifying 'case' articles (n=95), active controls (likely false positives, n=91), and passive controls (other true negatives, n=6796). The medical subject headings (MESH) that had been assigned to the articles in MEDLINE, and words used in titles and abstracts describing time-to-events were compared between the 'cases' and controls. Time-to-event words and figures within articles were also sought, and reporting quality was assessed. Search strategies to identify time-toevent articles were developed, using indexing terms and free-text words. An independent cohort of articles was used to validate the search strategies, consisting of 148 time-to-event articles handsearched from 6514 articles in the 50 dental journals with the highest impact factor for 2012. The findings of the research were used to draft guidance to improve reporting, which was circulated amongst 78 stakeholder experts for comment, and modified. Results: MeSH indexing of time-to-event analyses was inconsistent and inaccurate, author descriptions in abstracts and titles varied, and the quality of time-to-event reporting and graphics in the body of those articles was poor. The burden faced by someone wishing to find and use these articles was considered high. Sensitive, precise and optimized electronic search strategies were developed and validated with sensitivities up to 92% and precisions up to 93%. The draft guidance attracted comment from 46 experts across 15 countries, with approximately 90% of the 130 comments accepted into the revised version. The importance of good quality reporting was endorsed, and there was high interest in commending the guidance to authors, reviewers, and training dental specialists. Conclusions: This research programme explored how time-to-event dental articles were reported, and used those findings to suggest solutions that would help to improve the identification and use of these data, reducing research waste.

The research reported in this thesis is focused on some of the implications for Library and Information Science of the development of the Internet, and the World-Wide Web (WWW) in particular. It highlights the need for a wider involvement of this profession in the better management, organisation, and ultimately - usability, of the global information system. An extensive study of the information seeking process was carried out on a collection of interlinked hypertext documents constituting a PC-based model of the World-Wide Web. Special attention was paid to the correlation of WWW document authoring characteristics and the success rate in browsing sessions performed by novice, casual and experienced Internet end-users. The analysis of the results from a number of browsing sessions on the model led to some specific recommendations for comprehension-oriented WWW authoring. / Thesis (M.I.S.)-University of Natal, Pietermaritzburg, 1997.

In recent years the amount of information on the web has increased dramatically. As a result, it has become a challenge for the researchers to find effective ways that can help us query and extract meaning from these large repositories. Standard document search engines try to address the problem by presenting the users a ranked list of relevant documents. In most cases, this is not enough as the end-user has to go through the entire document to find out the answer he is looking for. Question answering, which is the retrieving of answers to natural language questions from a document collection, tries to remove the onus on the end-user by providing direct access to relevant information. This thesis is concerned with open-domain complex question answering. Unlike simple questions, complex questions cannot be answered easily as they often require inferencing and synthesizing information from multiple documents. Hence, we considered the task of complex question answering as query-focused multi-document summarization. In this thesis, to improve complex question answering we experimented with both empirical and machine learning approaches. We extracted several features of different types (i.e. lexical, lexical semantic, syntactic and semantic) for each of the sentences in the document collection in order to measure its relevancy to the user query. We have formulated the task of complex question answering using reinforcement framework, which to our best knowledge has not been applied for this task before and has the potential to improve itself by fine-tuning the feature weights from user feedback. We have also used unsupervised machine learning techniques (random walk, manifold ranking) and augmented semantic and syntactic information to improve them. Finally we experimented with question decomposition where instead of trying to find the answer of the complex question directly, we decomposed the complex question into a set of simple questions and synthesized the answers to get our final result. / x, 128 leaves : ill. ; 29 cm

Indiana University-Purdue University Indianapolis (IUPUI) / The contribution of this thesis focuses on addressing the challenges of improving and integrating the UniFrame Discovery Service (URDS) and Multi-level Matching (MLM) concepts. The objective was to find enhancements for both URDS and MLM and address the need of a comprehensive discovery service which goes beyond simple attribute based matching. It presents a detailed discussion on developing an enhanced version of URDS with MLM (proURDS). After implementing proURDS, the thesis includes details of experiments with different deployments of URDS components and different configurations of MLM. The experiments and analysis were carried out using proURDS produced MLM contracts. The proURDS referred to a public dataset called QWS dataset. This dataset includes actual information of software components (i.e., web services), which were harvested from the Internet. The proURDS implements the different matching operations as independent operators at each level of matching (i.e., General, Syntactic, Semantic, Synchronization, and QoS). Finally, a case study was carried out with the deployed proURDS. The case study addresses real world component discovery requirements from the earth science domain. It uses the contracts collected from public portals which provide geographical and weather related data.