Ecclesial ontology treats the origin, nature, existence, and destiny of the church. In the thought of T. F. Torrance, ecclesiology is derived from the doctrine of divine perfection. The church is given to participate in the fullness of God through the person of Jesus Christ. By framing his account in this way, Torrance issues a correction to the ecclesiological reductionism commonplace in the modern ecumenical movement. However, Torrance's particular construal of the Christ-church relation must be challenged. In his account, Christology is materially disproportioned in such a way that it threatens to absorb ecclesiological categories. Furthermore, Torrance undermines his ecumenical project, in which ecclesial unity is grounded in the mystery of the incarnation, by suggesting that the “mystery of iniquity” can prevent the actualization of this unity in space and time. Torrance's ecclesiology provides a rich resource for future constructive accounts to draw on, provided these accounts refine and revise said ecclesiology.

While remaining rooted in a comparison of some of the primary texts of the thinkers under scrutiny, my thesis also discusses several issues which arise in the mutual consideration of Heidegger and Strauss, specifically the questions of the ontological and political status of nature, the problem of ‘first philosophy,’ and the method by which to interpret philosophical texts, as well as a continuous analysis of Strauss’s appellation of ‘modern,’ as opposed to ‘ancient,’ and ‘religious,’ as opposed to ‘philosophical,’ to Heidegger’s thought. I first consider every moment in Strauss’s corpus where he discusses Heidegger’s thought. From this discussion, I identify four main lines of critique which may be extracted from Strauss’s writings on Heidegger. Then, I turn to Heidegger’s texts themselves in order to determine if Strauss’s critique indeed finds purchase there, addressing each of the lines of critique in turn. Finally, I consider Strauss and Heidegger in tandem, in light of the three questions identified above. I show that many of what Strauss determines to be Heidegger’s errors arose as a result of the way that Heidegger read ancient philosophical texts, and I suggest that Strauss’s approach, i.e., to consider the possible esoteric meaning of a text, in fact permits the reader to access an interpretation that is truer to the textual phenomena. This claim, however, is not intended to obscure the remarkable similarities between each thinker’s respective interpretive methods. I conclude that Strauss’s critique of Heidegger, vehement as it is, also indicates Strauss’s dependence on Heidegger’s thought for the inspiration of Strauss’s own philosophical project. The relation between Strauss and Heidegger, then, remains profoundly ambiguous.

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.

The current generation has seen technology penetrate every aspect of our life. However, even with recent advancements, adopters of contemporary technology are often angry and frustrated with their devices. With the increasing number of devices available to us in our day-to-day lives, and with the emergence of newer technologies like the Internet of Things, there is a stronger need than ever for computers to better understand human needs. However, there is still no machine understandable vocabulary that conceptualizes and describes the human-needs domain. As such, in this thesis we present a cloud-based ontology solution that conceptualizes the needs-domain by describing the relationships between the concepts of an Agent, a Role, a Need, and a Satisfier. The thesis focusses on the design of an OWL ontology which is based on an existing human-needs model. The human-needs model chosen for the ontology stems from a trans-disciplinary approach led by Manfred Max-Neef, called the Fundamental Human Needs model. It is seen as classifiable, finite and constant across all cultures and time periods. The methodology approach used to develop a new ontology is METHONTOLOGY, which is geared toward conceptualizing an ontology from scratch with the mindset of continual evaluation. We then further discuss the overall FHN Ontology comprising of various components including a RESTful Web Service and a SPARQL endpoint for querying and updating the FHN Ontology. The ontology is evaluated via competency questions for validation and via the Ontology Pitfall Scanner for verification and correctness across multiple criteria. The entire system is tested and evaluated by implementing a native android application which serves as a REST client to connect to the FHN Ontology end-point

With the popularity of the World Wide Web, a large amount of data is generated and made available through the Internet everyday. To integrate and query this huge amount of heterogeneous data, the vision of Semantic Web has been recognized as a possible solution. One key technology for the Semantic Web is ontologies. Many ontologies have been developed in recent years. Meanwhile, due to the demand of applications using multiple ontologies,  mappings between entities of these ontologies are generated as well, which leads to the generation of ontology networks consisting of ontologies and mappings between these ontologies. However, neither developing ontologies nor finding mappings between ontologies is an easy task. It may happen that the ontologies are not consistent or complete, or the mappings between these ontologies are not correct or complete, or the resulting ontology network is not consistent. This may lead to problems when they are used in semantically-enabled applications. In this thesis, we address two issues relevant to the quality of the mappings and the structure in the ontology network. The first issue deals with the missing mappings between networked ontologies. Assuming existing mappings between ontologies are correct, we investigate whether and how to use these existing mappings, to find more mappings between ontologies. We propose and test several strategies of using the given correct mappings to align ontologies. The second issue deals with the missing structure, in particular missing is-a relations, in networked ontologies. Based on the assumption that missing is-a relations are a kind of modeling defects, we propose an ontology debugging approach to tackle this issue. We develop an algorithm for detecting missing is-a relations in ontologies, as well as algorithms which assist the user in repairing by generating and recommending possible ways of repairing and executing the repairing. Based on this approach, we develop a system and test its use and performance.

No description available.

Due to the explosion of the amount of biomedical data, knowledge and tools that are often publicly available over the Web, a number of difficulties are experienced by biomedical researchers. For instance, it is difficult to find, retrieve and integrate information that is relevant to their research tasks. Ontologies and the vision of a Semantic Web for life sciences alleviate these difficulties. In recent years many biomedical ontologies have been developed and many of these ontologies contain overlapping information. To be able to use multiple ontologies they have to be aligned or merged. A number of systems have been developed for aligning and merging ontologies and various alignment strategies are used in these systems. However, there are no general methods to support building such tools, and there exist very few evaluations of these strategies. In this thesis we give an overview of the existing systems. We propose a general framework for aligning and merging ontologies. Most existing systems can be seen as instantiations of this framework. Further, we develop SAMBO (System for Aligning and Merging Biomedical Ontologies) according to this framework. We implement different alignment strategies and their combinations, and evaluate them in terms of quality and processing time within SAMBO. We also compare SAMBO with two other systems. The work in this thesis is a first step towards a general framework that can be used for comparative evaluations of alignment strategies and their combinations. / <p>Report code: LiU-Tek-Lic-2006:6.</p>

With the massive deluges of data that several domains of study experience, referred to as Big Data, the need to efficiently process and analyze data has risen. Ontologies have been employed to structure these domains with the ultimate purpose of generating new knowledge about the respective domain. Currently, when creating or examining an ontology, the concepts of the domain are limited to being statically defined in relation to other concepts. This limitation on the concept's definition affects the reasoning process by omitting or not properly representing all information that may exist in the domain such as how the constantly evolving environment changes how the data can be understood. The resulting tool of this research, Conto, allows for the interpretation of a concept as an abstract data type. When coupled to knowledge generation process, these interpretations allow the obtention of new knowledge that would traditionally be unobtainable. The different types of knowledge that can be obtained via the multiple interpretations are explored in this work using examples of ontologies. Conto is a Protege plugin that uses Ontograf to display the ontologies. / Thesis / Master of Applied Science (MASc)

Ontologies are widely used in various domains, including biomedical research, to structure information, represent knowledge, and analyze data. The combination of ontologies from different domains is crucial for systematic data analysis and comparison of similar domains. This process requires ontology composition, integration, and alignment, which involve creating new classes by reusing classes from different domains, aggregating types of ontologies within the same domain, and finding correspondences between ontologies within the same or similar domain. This thesis presents use cases where we applied ontology composition, integration, and alignment of phenotype ontologies, and evaluated the resulting ontologies and alignment. First, we analyzed a large aging dataset of inbred laboratory mice, using Mouse Anatomy and Mouse Pathology ontologies. Second, we integrated phenotype ontologies for human and model organism phenotypes to enable comparisons of phenotypes between and within individual species. We developed Pheno-e, an extension of PhenomeNet. We identified novel abnormal anatomical classes for fly phenotypes, allowing the annotation of fly genes that were not annotated before. We demonstrate the distinct contributions of each species' phenotypic data to detecting human diseases using Pheno-e, and show that mouse phenotypic data contributes the most to the discovery of gene--disease associations. This work could guide the selection of model organisms when building methods to find gene-disease associations. Additionally, we refined class definitions in phenotypic ontologies, specifically targeting cell cardinality phenotypes. This representation resolved incorrect inferences in the utilized ontologies, enabling accurate interpretation of phenotypic descriptions. Our findings reveal that this correction enhances gene-disease prediction for diseases associated with cardinality phenotypes. Third, we introduce a novel neural-symbolic method that combines logic fundamentals with machine learning for ontology alignment. This method begins with symbolic representation, followed by iterative neural learning for alignment and symbolic representation consistency checking and reasoning, and back to neural learning. We demonstrate that our system generates noncontroversial alignments first and these alignments are coherent with respect to OWL EL. This novel method can pave the way for more accurate and efficient ontology-based methods, which can have significant implications for various semantic web applications.

No description available.