Thesis (M.S.)--Middle East Technical University, 2003. / Keywords: Justification, Kinds of Justification, Truth, Truth Connection, Reality, Kinds of Reality, Knowledge, Conditions of Knowledge.

Thesis (M.S. in Computer Science)--Naval Postgraduate School, December 2002. / Thesis advisor(s): Neil C. Rowe, Xavier Maruyama. Includes bibliographical references (p. 105-109). Also available online.

Thesis--M.A., University of Hong Kong. / Mimeographed.

Thesis (M.S. in Information Technology Management)--Naval Postgraduate School, June 2003. / Thesis advisor(s): J.D. Fulp, Brian D. Steckler. Includes bibliographical references (p. 83). Also available online.

Thesis (M. Ed.)--York University, 2002. Graduate Programme in Education. / Typescript. Title on certificate page: Teaching question-based learning in elementary science education. Includes bibliographical references (leaves 100-102). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://wwwlib.umi.com/cr/yorku/fullcit?pMQ71573.

Recent epistemology has been dominated by the knowledge first approach championed by Timothy Williamson and others, and its influence continues to grow, spreading into the philosophy of language, the philosophy of mind, and beyond. Proponents of the knowledge first approach have argued for the centrality and importance of knowledge in these areas of philosophy by arguing that there is something wrong with asserting or believing something that one doesn’t know, that assertion and belief are to be understood in terms of knowledge, and that a knowledge‐maximizing principle of charity is constitutive of the contents of one’s assertions and beliefs. I attack the knowledge first approach by developing more plausible accounts of assertion, belief, and the determination of content that break these supposed ties with knowledge. / text

published_or_final_version / Business / Doctoral / Doctor of Philosophy

It has been debated whether knowledge attributions are descriptions of the world. Descriptivists argue that they are. Non-cognitivists discover that knowledge attributions have characteristics that are not shared by paradigm cases of descriptions. Most forms of non-cognitivism therefore deny that knowledge attributions are descriptions of the world. This thesis approaches the debate using data from the ordinary use of language. It is argued that a prominent form of descriptivism, attributor contextualism, is in conflict with treating ordinary use of simple knowledge attributions as true. This treatment of ordinary language is adopted by prominent contextualists, and is what distinguishes contextualism from its main rival, invariantism. The conflict is generalized to descriptivism in general so that either descriptivism or the treatment of simple ordinary use of the language as true has to be given up. Various arguments for and against such treatment of ordinary language and descriptivism’s alternative, non-cognitivism, are examined respectively. It is held that although many forms of non-cognitivism are problematic, whether non-cognitivism is a true thesis still remains open. It is also argued that despite its initial plausibility, the treatment of simple ordinary use of indicative language as true is not as attractive as it first appears to be. Since we are not forced to accept treating simple ordinary knowledge attributions as true, as far as the conflict between the two goes, we are not forced to give up descriptivism, either. However, non-cognitivism remains an attractive alternative to descriptivism. / published_or_final_version / Philosophy / Master / Master of Philosophy

Social media has been recognized as a potential platform for enhancing knowledge management (KM) on education due to its associated usefulness in encouraging active participation, collaboration, and relationship establishment. This thesis examines the incorporation of two popular social networking sites (SNSs), Blogs and Facebook, into the internship activities of two groups of students over six cohorts: 3 years of Blogs intern users (n = 47) and 3 years of Facebook intern users (n = 64). Behaviors, perceptions, KM processes and socio-emotional expressions of the intern users of Blogs and Facebook were analyzed and contrasted. A mixed research method was used to obtain quantitative and qualitative data through Blogs entries, Facebook posts, questionnaire and structured interviews. Results reveal that both Blogs and Facebook users engaged in regular use in writing, reading and commenting. Both groups of intern users perceived Blogs and Facebook to be useful as platforms for reflection, learning and communication. They also agreed that these two SNSs were able to facilitate emotional support and perceived support from peers through commenting. Qualitative analyses of Blogs entries and Facebook contents indicated that users of both Blogs and Facebook were able to engage in the processes of knowledge capture, knowledge sharing and dissemination, knowledge acquisition and knowledge application. Specifically, Blogs intern users had higher engagement on knowledge capture while Facebook intern users had higher engagement on knowledge sharing and dissemination. Users of both SNSs also actively engaged in emotional expressions and social support during their internship. Responses to open-ended questions suggested that participants had different preferences in using Blogs and Facebook. Blogs intern users put high values on the function of information recording and retrieving whereas Facebook intern users preferred the strong interaction features of Facebook. Overall, the study offers evidence on Facebook being appeared to be a better tool in facilitating KM processes and cultivating a knowledge sharing culture than blogs. It also demonstrates the potential similarities and differences of using Blogs and Facebook to enhance KM on education. / published_or_final_version / Library and Information Management / Master / Master of Science in Library and Information Management

A knowledge base, such as Yago or DBpedia, can be modeled as a large graph with nodes and edges annotated with class and relationship labels. Recent work has studied how to make use of these rich information sources. In particular, meta-paths, which represent sequences of node classes and edge types between two nodes in a knowledge base, have been proposed for such tasks as information retrieval, decision making, and product recommendation. Current methods assume meta-paths are found by domain experts. However, in a large and complex knowledge base, retrieving meta-paths manually can be tedious and difficult. We thus study how to discover meta-paths automatically. Specifically, users are asked to provide example pairs of nodes that exhibit high proximity. We then investigate how to generate meta-paths that can best explain the relationship between these node pairs. Since this problem is computationally intractable, we propose a greedy algorithm to select the most relevant meta-paths. We also present a data structure to enable efficient execution of this algorithm. We further incorporate hierarchical relationships among node classes in our solutions. Finally, we propose an effective similarity join algorithm in order to generate more node pairs using these meta-paths. Extensive experiments on real knowledge bases show that our approach captures important meta-paths in an efficient and scalable manner. / published_or_final_version / Computer Science / Master / Master of Philosophy