The classical search engine result page is used for many interactions with search results. While these are effective at communicating relevance, they do not present the context well. By giving the user an overview in the form of a spatialized display, in a domain that has a physical analog that the user is familiar with, context should become pre-attentive and obvious to the user. A prototype has been built that takes public medical information articles and assigns these to parts of the human body. The articles are indexed and made searchable. A visualization presents the coverage of a query on the human body and allows the user to interact with it to explore the results. Through usage cases the function and utility of the approach is shown.

In the last decade, a new wave of socio-technical cyber threats has emerged that is targeting both the technical and social vulnerabilities of organizations and requires fast and efficient threat mitigations. Yet, it is still common that financial organizations rely on yearly reviewed risk management methodologies that are slow and static to mitigate the ever-changing cyber threats. The purpose of this research is to explore the field of Dynamic Risk Management in Information Security from a socio-technical perspective in order to mitigate both types of threats faster and dynamically to better suit the connected world we live in today. In this study, the Design Science Research methodology was utilized to create a Dynamic Information Security Risk Management model based on functionality requirements collected through interviews with professionals in the financial sector and structured literature studies. Finally, the constructed dynamic model was then evaluated in terms of its functionality and usability. The results of the evaluation showed that the finalized dynamic risk management model has great potential to mitigate both social and technical cyber threats in a dynamic fashion. / Under senaste decenniet har en ny våg av sociotekniska cyberhot uppkommit som är riktade både mot de sociala och tekniska sårbarheterna hos organisationer. Dessa hot kräver snabba och effektiva hotreduceringar, dock är det fortfarande vanligt att finansiella organisationer förlitar sig på årligen granskade riskhanteringsmetoder som både är långsamma och statiska för att mildra de ständigt föränderliga cyberhoten. Syftet med denna forskning är att undersöka området för dynamisk riskhantering inom informationssäkerhet ur ett sociotekniskt perspektiv, med målsättningen att snabbare och dynamiskt kunna mildra bägge typerna av hot för att bättre passa dagens uppkopplade värld.  I studien användes Design Science Research för att skapa en dynamisk riskhanteringsmodell med syfte att hantera sociotekniska cyberhot mot informationssäkerheten. Riskhanteringsmodellen är baserad på funktionskrav insamlade genom intervjuer med yrkesverksamma inom finanssektorn, samt strukturerade litteraturstudier.  Avslutningsvis utvärderades den konstruerade dynamiska modellen avseende dess funktionalitet och användbarhet. Resultaten av utvärderingen påvisade att den slutgiltiga dynamiska riskhanteringsmodellen har en stor potential att mitigera både sociala och tekniska cyberhot på ett dynamiskt sätt.

Background: In the life sciences, the amount of literature and experimental data grows at a tremendous rate. In order to effectively access and integrate these data, biomedical ontologies – controlled, hierarchical vocabularies – are being developed. Creating and maintaining such ontologies is a difficult, labour-intensive, manual process. Many computational methods which can support ontology construction have been proposed in the past. However, good, validated systems are largely missing. Motivation: The biocuration community plays a central role in the development of ontologies. Any method that can support their efforts has the potential to have a huge impact in the life sciences. Recently, a number of semantic search engines were created that make use of biomedical ontologies for document retrieval. To transfer the technology to other knowledge domains, suitable ontologies need to be created. One area where ontologies may prove particularly useful is the search for alternative methods to animal testing, an area where comprehensive search is of special interest to determine the availability or unavailability of alternative methods. Results: The Dresden Ontology Generator for Directed Acyclic Graphs (DOG4DAG) developed in this thesis is a system which supports the creation and extension of ontologies by semi-automatically generating terms, definitions, and parent-child relations from text in PubMed, the web, and PDF repositories. The system is seamlessly integrated into OBO-Edit and Protégé, two widely used ontology editors in the life sciences. DOG4DAG generates terms by identifying statistically significant noun-phrases in text. For definitions and parent-child relations it employs pattern-based web searches. Each generation step has been systematically evaluated using manually validated benchmarks. The term generation leads to high quality terms also found in manually created ontologies. Definitions can be retrieved for up to 78% of terms, child ancestor relations for up to 54%. No other validated system exists that achieves comparable results. To improve the search for information on alternative methods to animal testing an ontology has been developed that contains 17,151 terms of which 10% were newly created and 90% were re-used from existing resources. This ontology is the core of Go3R, the first semantic search engine in this field. When a user performs a search query with Go3R, the search engine expands this request using the structure and terminology of the ontology. The machine classification employed in Go3R is capable of distinguishing documents related to alternative methods from those which are not with an F-measure of 90% on a manual benchmark. Approximately 200,000 of the 19 million documents listed in PubMed were identified as relevant, either because a specific term was contained or due to the automatic classification. The Go3R search engine is available on-line under www.Go3R.org.