In biomedical research, ontologies are widely used to represent knowledge as well as to annotate datasets. Many of the existing ontologies cover a single type of phenomena, such as a process, cell type, gene, pathological entity or anatomical structure. Consequently, there is a requirement to use multiple ontologies to fully characterize the observations in the datasets. Although this allows precise annotation of different aspects of a given dataset, it limits our ability to use the ontologies in data analysis, as the ontologies are usually disconnected and their combinations cannot be exploited. Motivated by this, here we present novel ontology design methods for combining pathology and anatomy concepts. To this end, we use a dataset of mouse models which has been characterized through two ontologies: one of them is the mouse pathology ontology (MPATH) covering pathological lesions while the other is the mouse anatomy ontology (MA) covering the anatomical site of the lesions. We propose four novel ontology design patterns for combining these ontologies, and use these patterns to generate four ontologies in a data-driven way. To evaluate the generated ontologies, we utilize these in ontology-based data analysis, including ontology enrichment analysis and computation of semantic similarity. We demonstrate that there are significant differences between the four ontologies in different analysis approaches. In addition, when using semantic similarity to confirm the hypothesis that genetically identical mice should develop more similar diseases, the generated combined ontologies lead to significantly better analysis results compared to using each ontology individually. Our results reveal that using ontology design patterns to combine different facets characterizing a dataset can improve established analysis methods.

This dissertation investigates three topics related to movement and events associated with geospatial barriers. This research makes a unique contribution to geographic information science (GIScience), by examining how boundaries influence the interactions of other geographic features and to location-based services by bringing a GIScience perspective to geofence services. Chapters 2, 3, and 4 can be considered independently as journal articles. Each of these chapters builds on the previous. Chapter 2 serves as the base by examining geospatial barriers and impedance events associated with these barriers. The relationships between barriers, the features they protect and the features that they hinder are formalized in an ontology design pattern (ODP) that also includes events associated with these features. Algorithms are written to demonstrate the ODP can be used to answer queries about the presence or absence of barrier events. Chapter 3 transitions from physical boundaries to digital boundaries by examining geofences, a location-based notification service. Interaction between users and multiple geofences are investigated when the configuration of geofences differs (i.e. disconnected, partially overlapping, and completely overlapping) and when geofences are independent or dependent (a rule states that the activation of geofence Y depends on users having visited geofence X). A geofence system is prototyped in an iOS environment and used to further evaluate user-geofence interactions. Chapter 4 expands on the topic of location- based notification regions by investigating what is required for a geofence system to handle events. How changes in conditions (e.g. the spreading of a flood or movement of a fire) interact with geofences and users and what new categories of interactions are required to handle these changes are discussed. The proposed system is applied to a scenario of flooding on multiple rivers interrupting the transportation network.

<p>Though a constantly increasing number of ontologies are now available on the Internet, the ontology construction process remains generally a manual task, so consequently an effort demanding task. As no unified ontology construction method is available in the literature, researchers started investigating different frameworks for automatically generating ontologies and, therefore shorten the time required for their construction. This master’s thesis presents a prototype system for automatic construction of ontology, based on ontology design patterns and unstructured texts, such as natural language texts. The use of ontology design patterns allow constructing well structured ontologies and reducing the demand of knowledge experts. A difference between our prototype system and the systems presented in the literature is, the possibility to increase accuracy of the generated ontology by selecting the more relevant terms and associations from the unstructured text and match them against the ontology design patterns. Also, a matching score is introduced to define the level of similarity between the terms extracted and the ontology design patterns. By setting a threshold value on the matching score, the relevant ontology design patterns are selected and used for the ontology construction process. The ontology construction framework used by the prototype system has been developed by the research group in Information Engineering of the School of Engineering, Jönköping University.</p>

<p>Though a constantly increasing number of ontologies are now available on the Internet, the ontology construction process remains generally a manual task, so consequently an effort demanding task. As no unified ontology construction method is available in the literature, researchers started investigating different frameworks for automatically generating ontologies and, therefore shorten the time required for their construction. This master’s thesis presents a prototype system for automatic construction of ontology, based on ontology design patterns and unstructured texts, such as natural language texts. The use of ontology design patterns allow constructing well structured ontologies and reducing the demand of knowledge experts. A difference between our prototype system and the systems presented in the literature is, the possibility to increase accuracy of the generated ontology by selecting the more relevant terms and associations from the unstructured text and match them against the ontology design patterns. Also, a matching score is introduced to define the level of similarity between the terms extracted and the ontology design patterns. By setting a threshold value on the matching score, the relevant ontology design patterns are selected and used for the ontology construction process. The ontology construction framework used by the prototype system has been developed by the research group in Information Engineering of the School of Engineering, Jönköping University.</p> / <p>Även fast ett ökande antal ontologier är tillgängliga på Internet, är ontologikonstruktion fortfarande till största delen en manuell process, som därigenom kräver en stor arbetsinsats. Eftersom ingen enhetlig metodologi för att konstruera ontologier finns i litteraturen, forskare började undersöka olika ansatser för att automatiskt generera ontologier och därigenom förkorta konstruktionstiden. Detta examensarbete presenterar ett prototypsystem för automatisk konstruktion av ontologier, baserat på designmönster för ontologier och ostrukturerad text (text i naturligt språk). Att använda designmönster ger en välstrukturerad ontologi och minskar behovet av expertkunskap. En skillnad mellan vårt system och system i litteraturen är möjligheten att få en mer korrekt ontologi genom att välja de mestrelevanta termerna och relationerna från texterna och matcha dem motdesignmönstren. Ett värde för överensstämmelsen har införts för att kunna beskriva hur stor likhet som finns mellan termerna och designmönstren. Genom att sätta ett tröskelvärde väljs de relevanta designmönstren ut och används för att konstruera ontologin. Den generella processen för ontologikonstruktion som används av prototypsystemet har utvecklats av forskningsgruppen i Informationsteknik vid Ingenjörshögskolan i Jönköping.</p>

Though a constantly increasing number of ontologies are now available on the Internet, the ontology construction process remains generally a manual task, so consequently an effort demanding task. As no unified ontology construction method is available in the literature, researchers started investigating different frameworks for automatically generating ontologies and, therefore shorten the time required for their construction. This master’s thesis presents a prototype system for automatic construction of ontology, based on ontology design patterns and unstructured texts, such as natural language texts. The use of ontology design patterns allow constructing well structured ontologies and reducing the demand of knowledge experts. A difference between our prototype system and the systems presented in the literature is, the possibility to increase accuracy of the generated ontology by selecting the more relevant terms and associations from the unstructured text and match them against the ontology design patterns. Also, a matching score is introduced to define the level of similarity between the terms extracted and the ontology design patterns. By setting a threshold value on the matching score, the relevant ontology design patterns are selected and used for the ontology construction process. The ontology construction framework used by the prototype system has been developed by the research group in Information Engineering of the School of Engineering, Jönköping University. / Även fast ett ökande antal ontologier är tillgängliga på Internet, är ontologikonstruktion fortfarande till största delen en manuell process, som därigenom kräver en stor arbetsinsats. Eftersom ingen enhetlig metodologi för att konstruera ontologier finns i litteraturen, forskare började undersöka olika ansatser för att automatiskt generera ontologier och därigenom förkorta konstruktionstiden. Detta examensarbete presenterar ett prototypsystem för automatisk konstruktion av ontologier, baserat på designmönster för ontologier och ostrukturerad text (text i naturligt språk). Att använda designmönster ger en välstrukturerad ontologi och minskar behovet av expertkunskap. En skillnad mellan vårt system och system i litteraturen är möjligheten att få en mer korrekt ontologi genom att välja de mestrelevanta termerna och relationerna från texterna och matcha dem motdesignmönstren. Ett värde för överensstämmelsen har införts för att kunna beskriva hur stor likhet som finns mellan termerna och designmönstren. Genom att sätta ett tröskelvärde väljs de relevanta designmönstren ut och används för att konstruera ontologin. Den generella processen för ontologikonstruktion som används av prototypsystemet har utvecklats av forskningsgruppen i Informationsteknik vid Ingenjörshögskolan i Jönköping.

Though a constantly increasing number of ontologies are now available on the Internet, the ontology construction process remains generally a manual task, so consequently an effort demanding task. As no unified ontology construction method is available in the literature, researchers started investigating different frameworks for automatically generating ontologies and, therefore shorten the time required for their construction. This master’s thesis presents a prototype system for automatic construction of ontology, based on ontology design patterns and unstructured texts, such as natural language texts. The use of ontology design patterns allow constructing well structured ontologies and reducing the demand of knowledge experts. A difference between our prototype system and the systems presented in the literature is, the possibility to increase accuracy of the generated ontology by selecting the more relevant terms and associations from the unstructured text and match them against the ontology design patterns. Also, a matching score is introduced to define the level of similarity between the terms extracted and the ontology design patterns. By setting a threshold value on the matching score, the relevant ontology design patterns are selected and used for the ontology construction process. The ontology construction framework used by the prototype system has been developed by the research group in Information Engineering of the School of Engineering, Jönköping University.

Ontologies are formal knowledge models that describe concepts and relationships and enable data integration, information search, and reasoning. Ontology Design Patterns (ODPs) are reusable solutions intended to simplify ontology development and support the use of semantic technologies by ontology engineers. ODPs document and package good modelling practices for reuse, ideally enabling inexperienced ontologists to construct high-quality ontologies. Although ODPs are already used for development, there are still remaining challenges that have not been addressed in the literature. These research gaps include a lack of knowledge about (1) which ODP features are important for ontology engineering, (2) less experienced developers' preferences and barriers for employing ODP tooling, and (3) the suitability of the eXtreme Design (XD) ODP usage methodology in non-academic contexts. This dissertation aims to close these gaps by combining quantitative and qualitative methods, primarily based on five ontology engineering projects involving inexperienced ontologists. A series of ontology engineering workshops and surveys provided data about developer preferences regarding ODP features, ODP usage methodology, and ODP tooling needs. Other data sources are ontologies and ODPs published on the web, which have been studied in detail. To evaluate tooling improvements, experimental approaches provide data from comparison of new tools and techniques against established alternatives. The analysis of the gathered data resulted in a set of measurable quality indicators that cover aspects of ODP documentation, formal representation or axiomatisation, and usage by ontologists. These indicators highlight quality trade-offs: for instance, between ODP Learnability and Reusability, or between Functional Suitability and Performance Efficiency. Furthermore, the results demonstrate a need for ODP tools that support three novel property specialisation strategies, and highlight the preference of inexperienced developers for template-based ODP instantiation---neither of which are supported in prior tooling. The studies also resulted in improvements to ODP search engines based on ODP-specific attributes. Finally, the analysis shows that XD should include guidance for the developer roles and responsibilities in ontology engineering projects, suggestions on how to reuse existing ontology resources, and approaches for adapting XD to project-specific contexts.

Ontologies are formal knowledge models that describe concepts and relationships and enable data integration, information search, and reasoning. Ontology Design Patterns (ODPs) are reusable solutions intended to simplify ontology development and support the use of semantic technologies by ontology engineers. ODPs document and package good modelling practices for reuse, ideally enabling inexperienced ontologists to construct high-quality ontologies. Although ODPs are already used for development, there are still remaining challenges that have not been addressed in the literature. These research gaps include a lack of knowledge about (1) which ODP features are important for ontology engineering, (2) less experienced developers' preferences and barriers for employing ODP tooling, and (3) the suitability of the eXtreme Design (XD) ODP usage methodology in non-academic contexts. This dissertation aims to close these gaps by combining quantitative and qualitative methods, primarily based on five ontology engineering projects involving inexperienced ontologists. A series of ontology engineering workshops and surveys provided data about developer preferences regarding ODP features, ODP usage methodology, and ODP tooling needs. Other data sources are ontologies and ODPs published on the web, which have been studied in detail. To evaluate tooling improvements, experimental approaches provide data from comparison of new tools and techniques against established alternatives. The analysis of the gathered data resulted in a set of measurable quality indicators that cover aspects of ODP documentation, formal representation or axiomatisation, and usage by ontologists. These indicators highlight quality trade-offs: for instance, between ODP Learnability and Reusability, or between Functional Suitability and Performance Efficiency. Furthermore, the results demonstrate a need for ODP tools that support three novel property specialisation strategies, and highlight the preference of inexperienced developers for template-based ODP instantiation---neither of which are supported in prior tooling. The studies also resulted in improvements to ODP search engines based on ODP-specific attributes. Finally, the analysis shows that XD should include guidance for the developer roles and responsibilities in ontology engineering projects, suggestions on how to reuse existing ontology resources, and approaches for adapting XD to project-specific contexts.

The use of semantic technologies and Semantic Web ontologies in particular have enabled many recent developments in information integration, search engines, and reasoning over formalised knowledge. Ontology Design Patterns have been proposed to be useful in simplifying the development of Semantic Web ontologies by codifying and reusing modelling best practices. This thesis investigates the quality of Ontology Design Patterns. The main contribution of the thesis is a theoretically grounded and partially empirically evaluated quality model for such patterns including a set of quality characteristics, indicators, measurement methods and recommendations. The quality model is based on established theory on information system quality, conceptual model quality, and ontology evaluation. It has been tested in a case study setting and in two experiments. The main findings of this thesis are that the quality of Ontology Design Patterns can be identified, formalised and measured, and furthermore, that these qualities interact in such a way that ontology engineers using patterns need to make tradeoffs regarding which qualities they wish to prioritise. The developed model may aid them in making these choices. This work has been supported by Jönköing University.

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