Thesis (Ph. D.)--Catholic University of America, 1939. / Secular name: Helen Cecilia O'Brien. Includes bibliographical references (p. 191-199).

Thesis (M.S.)--Brigham Young University. Dept. of Computer Science, 2006. / Includes bibliographical references (p. 43-46).

Thesis (Ph. L.)--Catholic University of America, 2004. / Includes bibliographical references (leaves 90-92).

Thesis (Ph. D.)--Ohio State University, 2005. / Title from first page of PDF file. Document formatted into pages; contains xi, 248 p. Includes bibliographical references (p. 244-248). Available online via OhioLINK's ETD Center

Thesis (M.S.)--Brigham Young University. Dept. of Computer Science, 2005. / Includes bibliographical references (p. 63-65).

Thesis (Ph. D.)--Florida State University, 2006. / Advisor: Kathleen Burnett, Florida State University, College of Information. Title and description from dissertation home page (viewed June 8, 2006). Document formatted into pages; contains x, 166 pages. Includes bibliographical references.

This paper describes the coupling of contexts and ontologies for semantic integration in the ECOIN semantic interoperability framework. Ontological terms in ECOIN correspond to multiple related meanings in different contexts. Each ontology includes a context model that describes how a generic ontological term can be modified according to contextual choices to acquire specialized meanings. Although the basic ECOIN concepts have been presented in the past, this paper is the first to show how ECOIN addresses the case of "single-ontology with multiple contexts" with an example of semantic integration using our new prototype implementation.

Schema and ontology matching play an important part in the field of data integration and semantic web. Given two heterogeneous data sources, meta data matching usually constitutes the first step in the data integration workflow, which refers to the analysis and comparison of two input resources like schemas or ontologies. The result is a list of correspondences between the two schemas or ontologies, which is often called mapping or alignment. Many tools and research approaches have been proposed to automatically determine those correspondences. However, most match tools do not provide any information about the relation type that holds between matching concepts, for the simple but important reason that most common match strategies are too simple and heuristic to allow any sophisticated relation type determination. Knowing the specific type holding between two concepts, e.g., whether they are in an equality, subsumption (is-a) or part-of relation, is very important for advanced data integration tasks, such as ontology merging or ontology evolution. It is also very important for mappings in the biological or biomedical domain, where is-a and part-of relations may exceed the number of equality correspondences by far. Such more expressive mappings allow much better integration results and have scarcely been in the focus of research so far. In this doctoral thesis, the determination of the correspondence types in a given mapping is the focus of interest, which is referred to as semantic mapping enrichment. We introduce and present the mapping enrichment tool STROMA, which obtains a pre-calculated schema or ontology mapping and for each correspondence determines a semantic relation type. In contrast to previous approaches, we will strongly focus on linguistic laws and linguistic insights. By and large, linguistics is the key for precise matching and for the determination of relation types. We will introduce various strategies that make use of these linguistic laws and are able to calculate the semantic type between two matching concepts. The observations and insights gained from this research go far beyond the field of mapping enrichment and can be also applied to schema and ontology matching in general. Since generic strategies have certain limits and may not be able to determine the relation type between more complex concepts, like a laptop and a personal computer, background knowledge plays an important role in this research as well. For example, a thesaurus can help to recognize that these two concepts are in an is-a relation. We will show how background knowledge can be effectively used in this instance, how it is possible to draw conclusions even if a concept is not contained in it, how the relation types in complex paths can be resolved and how time complexity can be reduced by a so-called bidirectional search. The developed techniques go far beyond the background knowledge exploitation of previous approaches, and are now part of the semantic repository SemRep, a flexible and extendable system that combines different lexicographic resources. Further on, we will show how additional lexicographic resources can be developed automatically by parsing Wikipedia articles. The proposed Wikipedia relation extraction approach yields some millions of additional relations, which constitute significant additional knowledge for mapping enrichment. The extracted relations were also added to SemRep, which thus became a comprehensive background knowledge resource. To augment the quality of the repository, different techniques were used to discover and delete irrelevant semantic relations. We could show in several experiments that STROMA obtains very good results w.r.t. relation type detection. In a comparative evaluation, it was able to achieve considerably better results than related applications. This corroborates the overall usefulness and strengths of the implemented strategies, which were developed with particular emphasis on the principles and laws of linguistics.

Information extraction (IE) is the process of automatically transforming written natural language (i.e., text) into structured information, such as a knowledge base. However, because natural language is inherently ambiguous, this transformation process is highly complex. On the other hand, as Information Extraction moves from the analysis of scientific documents to the analysis of Internet textual content, we cannot rely completely on the assumption that the content of the text is correct. Indeed, in contrast to scientific documents, which are peer reviewed, Internet content is not verified for the quality and correctness. Thus, two main issues that affect the IE process are the complexity of the extraction process and the quality of the data. In this dissertation, we propose an improved ontology-based IE (OBIE) by providing solutions to these issues of accuracy and content quality. Based on a hybrid strategy that combines aspects of IE that are usually considered as opposite to each other, or that are not even considered, we intend to improve IE by developing a more accurate extraction and new functionality (semantic error detection). Our approach is based on OBIE, a sub-area of IE, which reduces extraction complexity by including domain knowledge, in the form of concepts and relationships of the domain, to guide the extraction process. We address the complexity of extraction by combining information extractors that have different implementations. By integrating different types of implementation into one extraction system, we can produce a more accurate extraction. For each concept or relationship in the ontology, we can select the best implementation for extraction, or we can combine both implementations under an ensemble learning schema. In tandem, we address the quality of information by determining its semantic correctness with regard to domain knowledge. We define two methods for semantic error detection: by predefining the types of errors expected in the text or by applying logic reasoning to the text. This dissertation includes both published and unpublished coauthored material.

Thesis (DTech(Marketing Management)--Cape Peninsula University of Technology, 2012 / Scientific social inquiry beholds four dimensions of research, namely the epistemological, the ontological, the sociological, and the methodological dimension. Regardless of the approach taken, the goal of any scientific social inquiry is to produce knowledge that is as close as possible to the truth (implicitly or explicitly). Nonetheless, in ensuring that all scientific inquiry is truthful, it also has to be objective as well as rational.The nature of reality as it manifested itself in the new quantum physics changed irrevocably the dimensions of what constituted objectivity in science. Quantum physics, but more so the Heisenberg Principle was so cataclysmic that it not only attacked one or two conclusions about Classical Physics, but it completely replaced the very cornerstone and foundation on which the whole edifice of the Newtonian reality was based. In short, the Heisenberg Principle was the realisation that the conscious observer directly influenced what was being observed. In some mysterious fashion, the subject and the object were intimately united. It held a monistic worldview by proclaiming only oneness, and consequently relinquished the Cartesian dualism of the world.Hence, this research draws on the key postulate of oneness (monism) as put forward by quantum mechanics and questions the objectiveness, rationality and truthfulness of scientific inquiry. It does so by using a theoretical research design, i.e. a non-experimental conceptual historical inductive study. Providing an overview of monistic philosophy, it concludes that all is mind and the physical dualistic reality is a mere illusion. In reconciling the physical with the metaphysical worlds, the paper draws on Ouspensky’s Space-time and Mind model. By analysing the so-called fifth dimension of space, the research then proposes the inclusion of the Precognitive Research design, which augments the existing research designs (exploratory, descriptive and causal), and will enable scientists to come closer to truthful knowledge.