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This dissertation refines knowledge synthesis from publicly accessible databases, based on the model of D.R. Swanson. Knowledge synthesis endeavors bring together two or more non-interactive literatures to create combinatorial research data on a specific topic. In this endeavor the biomedical literature was searched on the anti-neoplastic agent nordihydroguaiaretic acid (NDGA) for its potential role as a functional food in the chemoprevention of breast cancer. Bibliometric cocitation was utilized to identify complementary but non-interactive literatures in the disciplines of biomedicine and dietary science. The continuing specialization and fragmentation of the cancer literature degenerates the potential usefulness of cross-disciplinary research and information. As the biomedical sciences become more specialized the potential increases for isolation of discoveries and for failures to connect science to the needs of the people. Within the information science discipline several techniques are available to bridge the isolation between discoveries recorded in different sets of literatures. Electronic database searching with combinatorial keyword entries, syllogistic modeling and bibliometric author cocitation analysis are the principle techniques applied in this endeavor. The research questions are addressed to the absence or presence of human in vivo research on breast cancer with the potentially chemopreventative functional food NDGA. Utilizing a syllogistic model the literatures of functional foods, nordihydroguaiaretic acid and breast cancer were searched with designated combinatorial keywords. The documents retrieved were subjected to author cocitation analysis to demonstrate disjointness or connectivity of the two complementary literatures. The results demonstrated a possible preventative relationship between breast cancer in women and nordihydroguaiaretic acid, a phytochemical antioxidant and potential functional food. The results of this study are consistent with D.R. Swanson's pioneering work in knowledge synthesis. Swanson's methods can be used to identify non-interactive, disjoint literatures. Continuing support for his techniques has been demonstrated.

Li Hoi-Ying. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (leaves 117-120). / Abstract --- p.i / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Background --- p.8 / Chapter 2.1 --- Strategies in Machine Translation Systems --- p.9 / Chapter 2.1.1 --- Direct MT Strategy --- p.10 / Chapter 2.1.2 --- Transfer MT strategy --- p.11 / Chapter 2.1.3 --- Interlingua MT Strategy --- p.13 / Chapter 2.1.4 --- AI Approach --- p.15 / Chapter 2.1.5 --- Statistical Approach --- p.15 / Chapter 2.2 --- Grammars --- p.16 / Chapter 2.3 --- Sublanguages --- p.19 / Chapter 2.4 --- Human Interaction --- p.21 / Chapter 2.5 --- Evaluation for Performance --- p.23 / Chapter 2.6 --- Machine Translation between Chinese and English --- p.25 / Chapter 2.7 --- Problems and Issues in MTCML --- p.29 / Chapter 2.7.1 --- Linguistic Characteristics of the Corpus --- p.29 / Chapter 2.7.2 --- Strategies for problems in MTCML --- p.31 / Chapter 3 --- Segmentation --- p.34 / Chapter 3.1 --- Strategies for Segmentation --- p.34 / Chapter 3.2 --- Segmentation algorithm in MTCML --- p.36 / Chapter 4 --- Tagging --- p.40 / Chapter 4.1 --- Objective --- p.40 / Chapter 4.2 --- Approach --- p.41 / Chapter 4.2.1 --- Category and Sub-category --- p.41 / Chapter 4.2.2 --- Tools --- p.45 / Chapter 5 --- Analysis --- p.48 / Chapter 5.1 --- Linguistic Study of the Corpus --- p.48 / Chapter 5.1.1 --- Imperative Sentences --- p.49 / Chapter 5.1.2 --- Elliptical Sentences --- p.50 / Chapter 5.1.3 --- Inverted Sentences --- p.52 / Chapter 5.1.4 --- Voice and Tense --- p.53 / Chapter 5.1.5 --- Vocabulary --- p.54 / Chapter 5.2 --- Pattern Extraction --- p.54 / Chapter 5.3 --- Pattern Reduction --- p.56 / Chapter 5.3.1 --- Case Study --- p.56 / Chapter 5.3.2 --- Syntactic Rules --- p.61 / Chapter 5.4 --- Disambiguation --- p.62 / Chapter 5.4.1 --- Category Ambiguity --- p.63 / Chapter 5.4.2 --- Structural Ambiguity --- p.65 / Chapter 6 --- Transfer --- p.68 / Chapter 6.1 --- Principle of Transfer --- p.68 / Chapter 6.2 --- Extraction of Templates --- p.71 / Chapter 6.2.1 --- Similarity Comparison --- p.72 / Chapter 6.2.2 --- Algorithm --- p.74 / Chapter 6.3 --- Classification of Templates --- p.76 / Chapter 6.3.1 --- Classification --- p.76 / Chapter 6.3.2 --- A Class-based Filter --- p.79 / Chapter 6.4 --- Transfer Rule-base --- p.80 / Chapter 6.4.1 --- Transfer Rules --- p.81 / Chapter 6.4.2 --- Rule Matching --- p.84 / Chapter 6.5 --- Chapter Summary --- p.85 / Chapter 7 --- Generation --- p.87 / Chapter 7.1 --- Sentence Generation --- p.87 / Chapter 7.2 --- Disambiguation of Homographs --- p.90 / Chapter 7.3 --- Sentence Polishing --- p.91 / Chapter 8 --- System Implementation --- p.95 / Chapter 8.1 --- Corpus --- p.95 / Chapter 8.2 --- Dictionaries and Lexicons --- p.97 / Chapter 8.3 --- Reduction Rules --- p.100 / Chapter 8.4 --- Transfer Rules --- p.102 / Chapter 8.5 --- Efficiency of the System --- p.104 / Chapter 8.6 --- Case Study --- p.105 / Chapter 8.6.1 --- Sample Result and Assessment --- p.105 / Chapter 8.6.2 --- Results of Segmentation and Tagging --- p.107 / Chapter 8.6.3 --- Results of Analysis --- p.108 / Chapter 8.6.4 --- Results of Transfer --- p.110 / Chapter 8.6.5 --- Results of Generation --- p.110 / Chapter 9 --- Conclusion --- p.112 / Bibliography --- p.117 / Chapter A --- Programmer's Guide --- p.121 / Chapter B --- Translation Instances --- p.125

Thesis (Ph.D.) - University of Glasgow, 2000. / Ph.D. thesis submitted to the Department of English Language, Faculty of Arts, University of Glasgow, 2000. Includes bibliographical references. Print version also available.

Thesis (Doctoral)--King's College (University of London), 1998. / BLDSC reference no.: D218276. Includes bibliographical references.

Thesis (M.A.)--Trent University, 1999. / Includes bibliographical references (leaves 149-172).

No description available.

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / Carta al editor. / Revisión por pares

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This research study is conducted to test Workman, Fiszman, Rindflesch and Nahl's information flow-serendipitous knowledge discovery (IF-SKD) model of information behavior, in a clinical care context. To date, there have been few attempts to model the serendipitous knowledge discovery of physicians. Due to the growth and complexity of the biomedical literature, as well as the increasingly specialized nature of medicine, there is a need for advanced systems that can quickly present information and assist physicians to discover new knowledge. The National Library of Medicine's (NLM) Lister Hill Center for Biocommunication's Semantic MEDLINE project is focused on identifying and visualizing semantic relationships in the biomedical literature to support knowledge discovery. This project led to the development of a new information discovery system, Spark. The aim of Spark is to promote serendipitous knowledge discovery by assisting users in maximizing the use of their conceptual short-term memory to iteratively search for, engage, clarify and evaluate information presented from the biomedical literature. Using Spark, this study analyzes the IF- SKD model by capturing and analyzing physician feedback. The McCay-Peet, Toms and Kelloway's Perception of Serendipity and Serendipitous Digital Environment (SDE) questionnaires are used. Results are evaluated to determine whether Spark contributes to physicians' serendipitous knowledge discovery and the ability of the IF-SKD ability to capture physicians' information behavior in a clinical setting.