Companies are today facing serious problems in dealing with information and data. At the same time knowledge created using data from different sources has evolved into a resource that few companies can afford to neglect. There is an obvious need to handle this conflict, as companies are basically forced into collecting and analysing data. If the companies fail to take advantage of this movement, they might quite quickly fall off the wagon [107, 157]. This statement can be supported by the fact that numerous companies are investing in R&D projects related to these issues. Even larger country-wide programs have been initiated, such as the Data to Intelligence (D2I) program, collecting over 25 large Finnish companies with the aim to “boost Finnish international competitiveness through intelligent (context- sensitive, personalized, proactive) data processing technologies and services that add measurable value”. Finding new ways to handle and analyse data is certainly a topic that generates interest1.
The main method for approaching this problem is to develop fuzzy ontologies to tackle knowledge management and knowledge mobilisation problems that are present in industrial cases. By modelling and storing tacit expert knowledge, it is possible to ease the knowledge losses as employees, for various reasons, leave the organisation. At the same time, these applications will make use of the mas- sive amounts of unused data available in companies. Carlsson et al. [56–58] state that knowledge mobilisation will change how we view knowledge management. Instead of collecting information from experts, and later distributing that informa- tion around the company, knowledge mobilisation allows for context-adaptive fast information whenever the user is in need of it.
Knowledge mobilisation usually has 4 different functionalities [56–58]: • The forming of new knowledge
• The development of new algorithm for knowledge mobilisation purposes • Finding knowledge in data (Big Data)
• Activate knowledge on the move
Being successful in all four phases could fully utilize the use of ICT for knowl- edge management. Fuzzy ontology has qualities that could offer a possible solution for this problem. The results presented in this thesis also hope to provide some
advances regarding the mobilisation of knowledge and what form it will take in modern organisations.
The first step of the research is to develop a fuzzy ontology that can be used as a test environment for experimenting to identify the benefits of fuzzy logic and ontologies. The Fuzzy Wine Ontology case by Carlsson et al. [56,57,59] is chosen to form the basis for the test environment to be developed. Wines and their possible food combinations are by nature imprecise, meaning that wine drinkers tend to listen quite a lot to experts and their advices. It has to be acknowledged that every individual has an unique taste regarding what wine they like the most, however, the aim is to show how tacit expert knowledge can be used to generate decision support. The test environment is adopted for use in other projects by adjusting it for the specific purposes and requirements; thereby testing the usefulness and adaptiveness of fuzzy ontologies.
As an increasing part of the knowledge stored in companies is expressed as expert knowledge, this has lately attracted an elevated amount of interest, espe- cially concerning the way knowledge management affects organisations. Com- panies will, at the latest, notice if they are lacking capabilities in managing and storing knowledge when their employees are approaching retirement age. When these experts leave the organization, they will take their knowledge with them. As a significant percentage of companies can be considered to depend on their internal knowledge for achieving success, they need to find suitable methods for keeping that acquired knowledge inside the organisation. This refers to both measured data as well as tacit knowledge. From an organisational point of view, it is essential to be able to make use of all this collected information in an automatic fashion. Experts tend to make use of linguistic expressions when communicating their opinion. By successfully modelling this linguistic data and combining it with precisely mea- sured information, one can create methods suitable for knowledge management purposes in organisations of today.
Aggregation operators are a vital part of the functions undergoing inside the fuzzy ontology applications, as they provide ways for combining different infor- mation instances. The family of Ordered Weighted Averaging operators (OWA operators) [307] is often used for aggregating values in models aimed for the Se- mantic Web. Creating new extensions of the OWA operator and thereby extending their scope is an important part of the development of fuzzy ontologies for support- ing decision making processes. The developed fuzzy ontology applications and new extensions of OWA operators are applied and adapted for various problems, illustrating how they could aid the decision making process.
Group Decision Making (GDM) situations are of special interest due to their complicated nature. When several decision makers are involved into the proce- dures it automatically means that other factors, such as weapons of influence and hierarchical structures, can influence the end result and undervalue opinions and in- formation. These tendencies are clearly visible in situations where the participants
are very knowledgeable, for instance a group of experts, in these situations other factors besides pure knowledge definitely influence the outcomes. An objective is to study how fuzzy ontologies could aid in making this process more effortless and smooth. This can be done for instance by reducing the set of alternatives that the group should decide among, or by providing suggestions on what alternative might be the most suitable, to form the basis for the experts’ decisions.
The research objective of this thesis can be summarized as to explore how fuzzy logic and ontologies could facilitate the exploitation and mobilisation of tacit knowledge and imprecise data in organisational and operational decision making processes. This is conducted focusing on the notion that there is a need to utilize the tacit expert knowledge abundant in today’s organisation, before it is too late. By showing how imprecise and tacit knowledge could be incorporated into deci- sion support systems, it can be demonstrated that this knowledge can be stored and utilized, even though the expert is no longer active. These tools provide organi- sations with considerable benefits and at the same time, they validate the fact that fuzzy ontologies are useful for modelling imprecise data and for creating applica- tions aimed for the Semantic Web. In the long run making fuzzy ontologies an usable tool also for the general user. The research questions presented in the next section offer more details regarding the different steps conducted in this thesis.