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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>

There are two significant impedances to the realization of the potential of ontologies. First, many ontology designers lack the necessary background in formal logics to express their intuitions clearly and precisely, resulting in the proliferation of ontologies with low expressivity. Concurrently, developing semantic mappings between existing ontologies is difficult, because much of the semantics is external to the representation. This thesis uses the idea of metaphor to develop architectures for ontology repositories to serve as bottom-up reusable resources. Moreover, an ontology design algorithm has been developed that allows designers to communicate their ideas at the semantic level, simply by generating and vetting models. Finally, a semantic mapping algorithm has been developed that uses an ontology repository to determine the similarities and differences between any number of target ontologies. An ontology for partial orders has been elaborated to demonstrate the proof of concept and populate the first iteration of the repository.

There are two significant impedances to the realization of the potential of ontologies. First, many ontology designers lack the necessary background in formal logics to express their intuitions clearly and precisely, resulting in the proliferation of ontologies with low expressivity. Concurrently, developing semantic mappings between existing ontologies is difficult, because much of the semantics is external to the representation. This thesis uses the idea of metaphor to develop architectures for ontology repositories to serve as bottom-up reusable resources. Moreover, an ontology design algorithm has been developed that allows designers to communicate their ideas at the semantic level, simply by generating and vetting models. Finally, a semantic mapping algorithm has been developed that uses an ontology repository to determine the similarities and differences between any number of target ontologies. An ontology for partial orders has been elaborated to demonstrate the proof of concept and populate the first iteration of the repository.

XDtools is a plugin originally used in the NeOn Toolkit ontology editor. XDtools is used as a tool for the ontology engineer when designing new ontologies or to add new parts to existing ontologies. The XDtools plugin was partially ported to another ontology editor, Protégé. This thesis describes the implementation and design choices made during the work with porting the plugin. A description of the parts that have been ported can be found in the report. It also contains the result of the testing and user study made after the implementation. Conclusions of those results and proposed changes to the plugin are also found in the report.

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.