Fuzzy ontology for knowledge mobilisation and decision support

A growing concern for organisations is how they should deal with increasing amounts of collected data. With fierce competition and smaller margins, organisations that are able to fully realize the potential in the data they collect can gain an advantage over the competitors. It is almost impossible to avoid imprecision when processing large amounts of data. Still, many of the available information systems are not capable of handling imprecise data, even though it can offer various advantages. Expert knowledge stored as linguistic expressions is a good example of imprecise but valuable data, i.e. data that is hard to exactly pinpoint to a definitive value. There is an obvious concern among organisations on how this problem should be handled; finding new methods for processing and storing imprecise data are therefore a key issue. Additionally, it is equally important to show that tacit knowledge and imprecise data can be used with success, which encourages organisations to analyse their imprecise data. The objective of the research conducted was therefore to explore how fuzzy ontologies could facilitate the exploitation and mobilisation of tacit knowledge and imprecise data in organisational and operational decision making processes. The thesis introduces both practical and theoretical advances on how fuzzy logic, ontologies (fuzzy ontologies) and OWA operators can be utilized for different decision making problems. It is demonstrated how a fuzzy ontology can model tacit knowledge which was collected from wine connoisseurs. The approach can be generalised and applied also to other practically important problems, such as intrusion detection. Additionally, a fuzzy ontology is applied in a novel consensus model for group decision making. By combining the fuzzy ontology with Semantic Web affiliated techniques novel applications have been designed. These applications show how the mobilisation of knowledge can successfully utilize also imprecise data. An important part of decision making processes is undeniably aggregation, which in combination with a fuzzy ontology provides a promising basis for demonstrating the benefits that one can retrieve from handling imprecise data. The new aggregation operators defined in the thesis often provide new possibilities to handle imprecision and expert opinions. This is demonstrated through both theoretical examples and practical implementations. This thesis shows the benefits of utilizing all the available data one possess, including imprecise data. By combining the concept of fuzzy ontology with the Semantic Web movement, it aspires to show the corporate world and industry the benefits of embracing fuzzy ontologies and imprecision.

Balancing creativity and risk intelligence in professional knowledge-intensive business services

The main strengths of professional knowledge-intensive business services (P-KIBS) are knowledge and creativity which needs to be fostered, maintained and supported. The process of managing P-KIBS companies deals with financial, operational and strategic risks. That is why it is reasonable to apply risk management techniques and frameworks in this context. A significant challenge hides in choosing reasonable ways of implementing risk management, which will not limit creative ability in organization, and furthermore will contribute to the process. This choice is related to a risk intelligent approach which becomes a justified way of finding the required balance. On a theoretical level the field of managing both creativity and risk intelligence as a balanced process remains understudied in particular within KIBS industry. For instance, there appears to be a wide range of separate models for innovation and risk management, but very little discussion in terms of trying to find the right balance between them. This study aims to shed light on the importance of well-managed combination of these concepts. The research purpose of the present study is to find out how the balance between creativity and risk intelligence can be managed in P-KIBS. The methodological approach utilized in the study is strictly conceptual without empirical aspects. The research purpose can be achieved through answering the following research supporting questions: 1. What are the characteristics and role of creativity as a component of innovation process in a P-KIBS company? 2. What are the characteristics and role of risk intelligence as an approach towards risk management process implementation in a P-KIBS company? 3. How can risk intelligence and creativity be balanced in P-KIBS? The main theoretical contribution of the study conceals in a proposed creativity and risk intelligence stage process framework. It is designed as an algorithm that can be applied on organizational canvas. It consists of several distinct stages specified by actors involved, their roles and implications. Additional stage-wise description provides detailed tasks for each of the enterprise levels, while combining strategies into one. The insights driven from the framework can be utilized by a vast range of specialists from strategists to risk managers, and from innovation managers to entrepreneurs. Any business that is designing and delivering knowledge service can potentially gain valuable thoughts and expand conceptual understanding from the present report. Risk intelligence in the current study is a unique way of emphasizing the role of creativity in professional knowledge-intensive industry and a worthy technique for making profound decisions towards risks.

Heading for Open Science: Filling the Knowledge Gap

Poster at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Testing FCM technique for the wind energy scenarios in Finland

In the latter days, human activities constantly increase greenhouse gases emissions in the atmosphere, which has a direct impact on a global climate warming. Finland as European Union member, developed national structural plan to promote renewable energy generation, pursuing the aspects of Directive 2009/28/EC and put it on the sharepoint. Finland is on a way of enhancing national security of energy supply, increasing diversity of the energy mix. There are plenty significant objectives to develop onshore and offshore wind energy generation in country for a next few decades, as well as another renewable energy sources. To predict the future changes, there are a lot of scenario methods developed and adapted to energy industry. The Master’s thesis explored “Fuzzy cognitive maps” approach in scenarios developing, which captures expert’s knowledge in a graphical manner and using these captures for a raw scenarios testing and refinement. There were prospects of Finnish wind energy development for the year of 2030 considered, with aid of FCM technique. Five positive raw scenarios were developed and three of them tested against integrated expert’s map of knowledge, using graphical simulation. The study provides robust scenarios out of the preliminary defined, as outcome, assuming the impact of results, taken after simulation. The thesis was conducted in such way, that there will be possibilities to use existing knowledge captures from expert panel, to test and deploy different sets of scenarios regarding to Finnish wind energy development.

“Why share in uncertain conditions?” Factors of individual level knowledge sharing in offshoring cases

The objective of this Master’s Thesis was to research factors influencing and enhancing individual level knowledge sharing in offshore projects which often involve uncertainty of the knowledge provider’s own future. The purpose was to understand why individuals are willing to share their knowledge under these kinds of circumstances. In addition the goal was to identify obstacles to interpersonal knowledge sharing in order to understand how to mitigate their influence. The research was conducted as a qualitative multiple case study in a global IT company, and the data was gathered using semi-structured personal theme interviews within two different offshore projects. In order to a gain a wider perspective on the matter, some management representatives were interviewed as well. Data was analysed with the inductive content analysis method. Results of the study indicate that individuals are willing to share their knowledge despite of uncertainty if they are motivated, if they are provided with opportunities to do so, and if they have skills, competence and experience to share their knowledge. A strong knowledge sharing culture in the organization or team also works as a strong incentive for individual level knowledge sharing. The findings suggest that even under uncertain conditions it is possible to encourage people to share their knowledge if uncertainty can be decreased to a bearable level, a robust and personal connection and relationship between the knowledge provider and acquirer can be created and suitable opportunities for knowledge sharing are provided. In addition, based on the results the support and commitment of management and HR in addition to favourable environmental circumstances play an essential role in building a bridge between the knowledge provider and acquirer in order to create a virtual environment and space for knowledge sharing: Ba.

Quality of radiotherapy care by development of e-feedback knowledge

The aim of this three phase study was to develop quality of radiotherapy care by the e-Feedback knowledge of radiotherapy -intervention (e-Re-Know). In Phase I, the purpose was to describe the quality of radiotherapy care and its deficits experienced by cancer patients. Based on the deficits in patient education in Phase II, the purpose was to describe cancer patients’ e-knowledge expectations in radiotherapy. In Phase III, the purpose was to develop and evaluate the outcomes of the e-Re-Know among breast cancer patients. The ultimate aim was to develop radiotherapy care to support patients’ empowerment with patient e-education. In Phase I (2004-2005), the descriptive design was used, and 134 radiotherapy patients evaluated their experiences by Good Nursing Care Scale for Patients (GNCS-P) in the middle of RT period. In Phase II (2006-2008), the descriptive longitudinal design was used and 100 radiotherapy patients’ e-knowledge expectations of RT were evaluated using open-ended questionnaire developed for this study before commencing first RT, in the middle of the treatment, and concluding RT period. In Phase III, firstly (2009-2010), the e-Re-Know intervention, i.e. knowledge test and feedback, was developed in terms of empowering knowledge and implemented with e-feedback approach based on literature and expert reviews. Secondly (2011-2014), the randomized controlled study was used to evaluate the e-Re-Know. Breast cancer patients randomized to either the intervention group (n=65) receiving the e-Re-Know by e-mail before commencing first RT and standard education or the control group (n=63) receiving standard education. The data were collected before commencing first RT, concluding last RT and 3 months after last RT using RT Knowledge Test, Spielberger’s State Trait Inventory (STAI) and Functional Assessment of Cancer Therapy - Breast (FACT-B) –instruments. Data were analyzed using statistical methods and content analysis. The study showed radiotherapy patients experienced quality of care high. However, there were deficits in patient education. Furthermore, radiotherapy patients’ multidimensional e-knowledge expectations through Internet covered mainly bio-physiological and functional knowledge. Thus, the e-Re-Know was developed and evaluated. The study showed when breast cancer patients’ carried out the e-Re-Know their knowledge of side effects self-care was significantly increased and quality of life (QOL) significantly improved in line with decrease in anxiety from time before radiotherapy period to three months after. In addition, the e-Re-Know has potential to have positive effects on anxiety and QOL, regardless of patient characteristics or knowledge level. The results support the theory of empowering patient education suggesting that empowerment can be supported by confirming patients’ understanding of own knowledge level. In summary, the e-Feedback knowledge of radiotherapy (e-Re-Know) intervention can be recommended in development of quality of radiotherapy care experienced by breast cancer patients. Further research is needed to assess and develop patient-centred quality of care by patient education among cancer patients.

Organizing for Systemic Innovations – Research on Knowledge, Interaction and Organizational Interdependencies

Systemic innovation has emerged as an important topic due to the interconnected technological and sociotechnical change of our current complex world. This study approaches the phenomenon from an organizing perspective, by analyzing the various actors, collaborative activities and resources available in innovation systems. It presents knowledge production for innovation and discusses the organizational challenges of shared innovation activities from a dynamic perspective. Knowledge, interaction, and organizational interdependencies are seen as the core elements of organizing for systemic innovations. This dissertation is divided into two parts. The first part introduces the focus of the study and the relevant literature and summarizes conclusions. The second part includes seven publications, each reporting on an important aspect of the phenomenon studied. Each of the in-depth single-case studies takes a distinct and complementary systems approach to innovation activities – linking the refining of knowledge to the enabling of organizations to participate in shared innovation processes. These aspects are summarized as theoretical and practical implications for recognizing innovation opportunities and turning ideas into innovations by means of using information and organizing activities in an efficient manner. Through its investigation of the existing literature and empirical case studies, this study makes three main contributions. First, it describes the challenges inherent in utilizing information and transforming it into innovation knowledge. Secondly, it presents the role of interaction and organizational interdependencies in innovation activities from various novel perspectives. Third, it highlights the interconnection between innovations and organizations, and the related path dependency and anticipatory aspects in innovation activities. In general, the thesis adds to our knowledge of how different aspects of systems form innovations through interaction and organizational interdependencies. It highlights the continuous need to redefine information and adjust organizations and networks based on ongoing activities – stressing the emergent, systemic nature of innovation.

Liber fostering Open Science and Knowledge Discovery

Presentation of Kristiina Hormia-Poutanen at the 25th Anniversary Conference of The National Repository Library of Finland, Kuopio 22th of May 2015.

The northern provinces of Sweden and Norway with part I, of Russia / published under the superintendence of the Society for the Diffusion of Useful Knowledge.

[N. 1:4400000].

Effect of the Knowledge Representation Form on the Efficiency of Knowledge Sharing

The goal of this thesis is to estimate the effect of the form of knowledge representation on the efficiency of knowledge sharing. The objectives include the design of an experimental framework which would allow to establish this effect, data collection, and statistical analysis of the collected data. The study follows the experimental quantitative design. The experimental questionnaire features three sample forms of knowledge: text, mind maps, concept maps. In the interview, these forms are presented to an interviewee, afterwards the knowledge sharing time and knowledge sharing quality are measured. According to the statistical analysis of 76 interviews, text performs worse in both knowledge sharing time and quality compared to visualized forms of knowledge representation. However, mind maps and concept maps do not differ in knowledge sharing time and quality, since this difference is not statistically significant. Since visualized structured forms of knowledge perform better than unstructured text in knowledge sharing, it is advised for companies to foster the usage of these forms in knowledge sharing processes inside the company. Aside of performance in knowledge sharing, the visualized structured forms are preferable due the possibility of their usage in the system of ontological knowledge management within an enterprise.

Quantitative refinement of reaction-based biomodels

In the field of molecular biology, scientists adopted for decades a reductionist perspective in their inquiries, being predominantly concerned with the intricate mechanistic details of subcellular regulatory systems. However, integrative thinking was still applied at a smaller scale in molecular biology to understand the underlying processes of cellular behaviour for at least half a century. It was not until the genomic revolution at the end of the previous century that we required model building to account for systemic properties of cellular activity. Our system-level understanding of cellular function is to this day hindered by drastic limitations in our capability of predicting cellular behaviour to reflect system dynamics and system structures. To this end, systems biology aims for a system-level understanding of functional intraand inter-cellular activity. Modern biology brings about a high volume of data, whose comprehension we cannot even aim for in the absence of computational support. Computational modelling, hence, bridges modern biology to computer science, enabling a number of assets, which prove to be invaluable in the analysis of complex biological systems, such as: a rigorous characterization of the system structure, simulation techniques, perturbations analysis, etc. Computational biomodels augmented in size considerably in the past years, major contributions being made towards the simulation and analysis of large-scale models, starting with signalling pathways and culminating with whole-cell models, tissue-level models, organ models and full-scale patient models. The simulation and analysis of models of such complexity very often requires, in fact, the integration of various sub-models, entwined at different levels of resolution and whose organization spans over several levels of hierarchy. This thesis revolves around the concept of quantitative model refinement in relation to the process of model building in computational systems biology. The thesis proposes a sound computational framework for the stepwise augmentation of a biomodel. One starts with an abstract, high-level representation of a biological phenomenon, which is materialised into an initial model that is validated against a set of existing data. Consequently, the model is refined to include more details regarding its species and/or reactions. The framework is employed in the development of two models, one for the heat shock response in eukaryotes and the second for the ErbB signalling pathway. The thesis spans over several formalisms used in computational systems biology, inherently quantitative: reaction-network models, rule-based models and Petri net models, as well as a recent formalism intrinsically qualitative: reaction systems. The choice of modelling formalism is, however, determined by the nature of the question the modeler aims to answer. Quantitative model refinement turns out to be not only essential in the model development cycle, but also beneficial for the compilation of large-scale models, whose development requires the integration of several sub-models across various levels of resolution and underlying formal representations.