This thesis addresses the problem of tool support for semi-automatic ontology mapping and merging. Solving this problem contributes to ontology creation and evolution by relieving users from tedious and time-consuming work. This thesis shows that a tool can be built that will take a “mini-ontology” and a “growing ontology” as input and make it possible to produce manually, semi-automatically, or automatically an extended growing ontology as output. Characteristics of this tool include: (1) a graphical, interactive user interface with features that will allow users to map and merge ontologies, and (2) a framework supporting pluggable, semi-automatic, and automatic mapping and merging algorithms.

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Data integration systems provide uniform access to a set of heterogeneous structured data sources. An essential component of a data integration system is the mapping between the federated data model and each data source. The scale of interconnect among data sources in the big data era is a new impetus for automating the mapping process. Despite decades of research on data integration, generating mappings still requires extensive labor. The thesis of this research is that the progress on automatic data integration has been limited by a narrow definition of mapping. The common mapping process is to find correspondences between pairs of entities in the data models, and create logic expressions over the correspondences as executable mappings. This does not cover all issues in real world applications. This research aims to overcome this problem in two ways: (1) generalize the common mapping definition for relational databases; (2) address the problem in a more general framework, the Semantic Web. The Semantic Web provides flexible graph based data models and reasoning capabilities as in knowledge representation systems. The new graph data model introduces opportunities for new mapping definitions. The comparison of mapping definitions and solutions for both relational databases and the Semantic Web is discussed. In this dissertation, I propose two generalizations of mapping problems. First, the common schema matching definition for relational databases is generalized from finding correspondences between pairs of attributes to finding correspondences consisting of relations, attributes, and data values. This generalization solves real world issues that are not previously covered. The same generalization can be applied to ontology matching in the Semantic Web. The second piece of work generalizes the ontology mapping definition from finding correspondences between pairs of entities to pairs of graph paths (sequences of entities). As a path provides more context than a single entity, mapping between paths can solve two challenges in data integration: the missing mapping challenge and the ambiguous mapping challenge. Combining the two proposed generalizations together, I demonstrate a complete data integration system using the Semantic Web techniques. The complete system includes the components of automatic ontology mapping and query reformulation, and semi-automatically federates the query results from multiple data sources. / text

This thesis argues for the idea that there are deep interconnections between the notions of ontology and reflexivity. It starts from the idea that ontological claims are cognitionally prior to epistemological and methodological accounts. It is argued that ontology is of particular importance to social science because the boundary between the substantive and the ontological is less clear than in natural science. Furthermore, because social science is located within its object, society, it is argued that self-referential questions about the epistemic status of every social ontology emerge. In the face of these self-referential questions concerning ontological coherence, the ‘epistemic criterion of reflexivity’ is proposed in this thesis. Meeting this criterion is required to deal successfully with the self-referential problem emerging from the fact that the knowing subject is part of her object. I argue that it is only by conceptualizing agents as self-reflective knowing subjects that an ontology has a chance of satisfying the criterion of epistemic reflexivity which is proposed by this thesis. In Chapters 1 to 3, the works of Roy Bhaskar, Pierre Bourdieu, Jügen Habermas, Alvin Gouldner and Andrew Sayer, as well as of several social constructionists and ethnomethodologists are examined, considering their contribution to the notions of ontology and epistemic reflexivity. It is argued that proponents of both relativistic and deterministic social theories cannot satisfy the criterion of epistemic reflexivity because they cannot coherently account for their knowledge-claims using their own ontologies. I thus argue that it is not enough for a social theory to provide an account of self-reflection – for the wider ontology in which it is situated may itself deny the possibility of such a self-reflective activity. It is in this sense that I argue for the need for an improved conceptualization of self-reflection in which agents are conceptualized as having the capacity of self-objectivation within context. It is through having such a presupposition that ontologies can fulfill the epistemic criterion of reflexivity proposed. The need for such a conceptualization of self-reflection leads me to explore two relevant approaches in Chapters 4 and 5, those of Archer and Castoriadis. I begin by looking at Margaret Archer’s account of the ‘internal conversation’. However, Archer’s internal dialogue will be shown problematic in the sense that it results in various contradictory claims. The thesis then considers Cornelius Castoriadis’ notion of self-reflective imagination which partially meets the epistemic criterion of reflexivity proposed in this thesis.

This paper discusses the development of a new information representation system embodied in ontology and the Semantic Web. The new system differs from other representation systems in that it is based on a more sophisticated semantic representation of information, aims to go well beyond the document level, and designed to be understood and processed by machine. A common theme underlying these three features, i.e., turning documents into meaningful interchangeable data, reflects a rising use expectation nurtured by modern technology and, at the same time, presents a unique challenge for its enabling technologies.

Digital Libraries and Digital Repositories are data-intensive with large numbers of fulltext resources accessible online. Activities in the area of Semantic Web development recognize the significant part played by metadata and knowledge organization systems such as classification systems and thesauri in capturing and communicating ‘meaning’. We now have Web ontology standards, such as Simple Knowledge Organization Systems (SKOS), a common data model for sharing and linking knowledge organization systems via the Semantic Web. Standards such as SKOS are also meant to be used as a vehicle for deployment of knowledge organization systems that were not born digital (or XML/RDF) such as thesauri and bibliographic classifications. This paper attempts to present an application of the faceted classification scheme as enunciated by Ranganathan in developing ontologies. It further explores the issues in modelling the faceted scheme of Ranganathan using SKOS.

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Advantages of applying information and communication technologies to support complex team practices in healthcare domain have often been supported in the extant literature. The primary assumption is that before putting any technologies in place to support team functions, the team-based environment should be completely modeled. To date, many frameworks have been proposed for modeling healthcare teams; however, most of the frameworks only focus on single or a few aspects of teamwork and the outcomes usually present overlaps, limitations and inconsistencies. As a result, there is an increasing demand for offering an overarching framework that integrates the multiple dimensions of healthcare teamwork into a synthetic whole and clearly conceptualizes the potentially important relationships and dependencies that exist over those dimensions. In order to properly address the aforementioned challenge, this thesis applies ontological engineering to develop an overarching framework for integrating the multiple dimensions of teamwork concept in healthcare domain. For this purpose, we first illustrate a set of four stage methodological approach to provide explicit details on how to incorporate a theatrical foundation into the ontology. Then, the proposed approach is used to develop a derived ontological framework. Finally, accuracy and completeness of the proposed ontology based framework is validated to show that it is able to accurately represent the domain is it being employed for. The values and capabilities of ontology have already been studied and approved, and this technology is known as the best sources to represent a knowledge domain by means of concepts and accurately define the relationships among them. Our aim in this thesis is to further research how to develop and evaluate a standard ontology based framework to facilitate the healthcare team modeling.