Thesis (Ph. D.)--University of Western Ontario, 1997. / Includes bibliographical references (leaves 164-181).

Universiẗat, Diss., 2002--Bonn.

University, Diss., 2003--Bonn.

Techn. Hochsch., Diss., 2004--Aachen.

University, Diss., 2004--Bremen.

Thesis (Ph. D.)--University of California, San Diego, 2006. / Title from first page of PDF file (viewed December 7, 2006). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 447-467).

No description available.

Thesis (PhD) -- AUT University, 2008. / Primary supervisor: Dr. Andy M. Connor. Includes bibliographical references. Also held in print ( 248 leaves ; 30 cm.) in the Archive at the City Campus (T 025.524 LIM )

Inefficiencies associated with online information search are becoming increasingly prevalent in digital environments due to a surge in Consumer Generated Content (CGC). Despite growing scholarly interest in investigating users' information search behavior in CGC environments, there is a paucity of studies that explores the phenomenon from a theory-guided angle. Drawing on Information Foraging Theory (IFT), we re-conceptualize online information search as a form of adaptive user behavior in response to system design constraints. Through this theoretical lens, we advance separate taxonomies for online information search tactics and strategies, both of which constitute essential building blocks of the search process. Furthermore, we construct a research framework that bridges the gap between online information search tactics and strategies by articulating how technology-enabled search tactics contribute to the fulfillment of strategic search goals. We validate our research framework via an online experiment by recruiting participants from Amazon Mechanical Turk (AMT). Participants were tasked to perform searches on custom-developed online review websites, which were modeled after a popular online review website and populated with real restaurant review data. Empirical findings reveal that the provision of different search features indeed engenders distinct search tactics, thereby allowing users varying levels of search determination control and search manipulation control. In turn, both types of search controls affects users' result anticipation and search costs, which when combined, determine the efficiency of goal-oriented search strategy and the utility of exploratory search strategy. This study provides valuable insights that can guide future research and practice.

xiii, 149 p. : ill. (some col.) / Information extraction (IE) aims to recognize and retrieve certain types of information from natural language text. For instance, an information extraction system may extract key geopolitical indicators about countries from a set of web pages while ignoring other types of information. IE has existed as a research field for a few decades, and ontology-based information extraction (OBIE) has recently emerged as one of its subfields. Here, the general idea is to use ontologies--which provide formal and explicit specifications of shared conceptualizations--to guide the information extraction process. This dissertation presents two novel directions for ontology-based information extraction in which ontologies are used to improve the information extraction process. First, I describe how a component-based approach for information extraction can be designed through the use of ontologies in information extraction. A key idea in this approach is identifying components of information extraction systems which make extractions with respect to specific ontological concepts. These components are termed "information extractors". The component-based approach explores how information extractors as well as other types of components can be used in developing information extraction systems. This approach has the potential to make a significant contribution towards the widespread usage and commercialization of information extraction. Second, I describe how an ontology-based information extraction system can make use of multiple ontologies. Almost all previous systems use a single ontology, although multiple ontologies are available for most domains. Using multiple ontologies in information extraction has the potential to extract more information from text and thus leads to an improvement in performance measures. The concept of information extractor, conceived in the component-based approach for information extraction, is used in designing the principles for accommodating multiple ontologies in an ontology-based information extraction system. / Committee in charge: Dr. Dejing Dou, Chair; Dr. Arthur Farley, Member; Dr. Michal Young, Member; Dr. Monte Westerfield, Outside Member