||IT Context: Information technologies are at present used in various disciplines to address issues such as information processing, data mining, knowledge-modelling etc. Its final goal is to provide necessary aid to professionals during decision-making process. This raises already few questions such as, what type of data is considered and are there some new emerging technologies that can improve knowledge modelling and therefore provide better decision support to the professionals.Design professionals are very often confronted with soft data that they somehow need to interpret and finally integrate in a design. Situations dealing with the numerical data may occur quite naturally in exact sciences like engineering sciences, life sciences etc. However, the quantities subject to consideration in soft sciences are often qualitative rather than quantitative so that we relate to that type of data as 'soft' data. As an example, in such cases, the quantities may be linguistic so that such quantities have to be somehow expressed in numerical form for treatment by conclusive numerical analysis methods.Objectives: The architectural design task is one example having linguistic qualities as priory design information. This is especially the case when qualities of certain space are discussed, like for example in post occupancy evaluation of the buildings, where the relationship between spatial characteristics and psychological aspects plays an important role. Expressions such as: bright colour, light room, large space are some of these examples and therefore a special method is needed for representation and processing of such vague expressions and concepts. Better understanding of these concepts is necessary so that the knowledge can be modelled in a proper way.Methodology: The analyses are performed by means of soft computing methods. The data subject to analysis and later to knowledge modelling belongs to an underground station that is already being used. For this purpose, the data on psychological aspects are obtained via comprehensive inquiry of the users of underground station. For the analysis, the linguistic information is firstly converted to terms in fuzzy logic domain and after appropriate treatment, the data analyses are carried out and the results are expressed in most comprehensible form for design assessments. Such conversions are referred to as fuzzification and defuzzification, where the data are expressed in numerical form and therefore become convenient for mathematical treatment.Conclusions: Referring to the complexity of task in dealing with the soft data as well as dealing with soft computing, the paper first identifies the source of these complexities referring to the architectural design tasks. Following this, a soft computing analysis method based on one case study will be presented, whereby the focus will be on knowledge modelling. Finally, the results of the analyses together with the conclusions regarding the observed effectiveness of the approach are presented.