Search and Comparison

The KMF provides an interface for the search of behavioural patterns via keywords. The functionality enables creators to search for suitable behavioural patterns in the domain- specific and common categories. The result of the search is a list containing a set of related behavioural patterns. The searching process itself compares the provided keyword with the keywords of the metainformation from the knowledge entries of those categories. However, this approach will cause some problems.

Collaboration and knowledge sharing increases the complexity of the knowledge repository in regard to the management and processing of the knowledge flow. The reason for this is the fact that every person uses his own vocabulary for the description and labelling of the activities. It is often the case that people use different words to describe the same

activity. This synonymy of words is a huge problem for the comparison of knowledge and information. thinking contemplating pondering editing writing typing

Figure 5.8: Synonymy of Activities

Examples of synonymy are presented in Figure 5.8. The two boxes contain words with a similar meaning. Each word of the left box describes a thinking activity. Those words are slightly different in their meaning. Nonetheless, a creator, searching for patterns related to the thinking activity, will be also interested in patterns which are described by pondering and contemplating. The words in the right box have a similar relation to each other. In fact, there exist related activities for most of the entries in every creator’s list of activities.

Another problem arises through the granularity of the list of activities. Some creators just use a few activities for the mapping of the creative process. Others might use a more fine- grained set of activities. Using several activities, where others would use one to describe the same cases. An example is the activity discussing. Some people differentiate between relaxed chatting with friends and discussing with colleagues.

The KMF must be able to provide all closely related entries for a keyword. This requires information about the relations of the words. It is not possible to identify those similarities with only the help of an algorithm. There are several techniques available, which try to solve these problems. Many of them solve the problem of synonymy, but cannot cope with the granularity of the activities. A solution for this problem requires a corpora of words and their relations. This can be either provided by lexical or ontology based approaches or Self-Organizing Map (SOM). This problem has been discussed in detail in Chapter 2.

The KMF uses search facilities based on the Wordnet corpora. This approach has a huge benefit in opposite to the SOM approach. It provides a large set of data. Furthermore, there is no need for a training of the data, before using the system. The SOM approach requires all these steps.

5.4

Summary

This chapter introduced the concept of a knowledge repository. The reason for the devel- opment of a knowledge repository arose from the discussion of current creativity support tools in Chapter 2. One of the main limitations of current approaches is the missing of techniques for a store of knowledge. Without this, it is impossible to store and broadly communicate novel findings about the creative process, gained through the usage of the creativity support tool.

The introduced knowledge repository contains a knowledge database and a knowledge management facility. The knowledge database is responsible for the storage of the knowl- edge itself. Its centralised design enables the exchange of knowledge via networks.

A facility for the management of the knowledge provides interfaces for the adding of new knowledge, change and retrieval of existing knowledge. Moreover, it automatically detects identical patterns within the knowledge entries of creators from the same domain. The most important feature, however, is the exchange of knowledge through shared domain and common knowledge entries. The facility is supporting the exchange and therefore the growth of knowledge. This is an important improvement in regard to current creativity support tools.

Detailed descriptions about the structure and meaning of knowledge for the analysis pro- cess are provided. The knowledge, stored in this repository, consists of two types:

ˆ Pattern expression ˆ Metainformation

Each knowledge entry consists of a BPDL expression with a set of associated metainfor- mation. The expressions, describing the behavioural patterns are an important part of the knowledge entries. These expressions describe the structure of the wanted informa- tion. The purpose of the expressions and the gained result sets is described through the metainformation.

The last section of this chapter discusses the exchange of knowledge in detail. A key focus lies on the benefits which will arise from a centralised knowledge repository with shared data sets. Different solutions for tackling word synonymy have been discussed.

Analysis and Support of the

Creative Process

Objectives

ˆ Analysis of the creative behaviour by using behavioural patterns.

ˆ Introduction of a novel framework for the support of the creative process. ˆ Discussion of the analysis possibilities.

ˆ Comparison of creativity maps.

“Clearly no group can as an entity create ideas. Only individuals can do this. A group of individuals may, however, stimulate one another in the creation of ideas.”

6.1

Introduction

This chapter is answering the research questions number four: “What type of information can be extracted from the creativity maps?” and five: “How can the results of the analyses be used for the support of the creative process?”. The chapter is divided into two main parts. The first part describes the possibilities for analysing the creative behaviour and comparison of creativity maps. The second part describes how the results of the analysis process can benefit the support of the creative process.

The scope of application for the results of the information extraction process will be described in detail. This includes the first step of analysing the extracted information. The creator can undertake the analysis at two stages, during his/her work and more detailed afterwards. Moreover, creativity maps enable the analysis of differences between creativity maps through a comparison. Finally, an introduction of a concept for the support of the creative process which is realised through the analysis of creativity maps with behavioural patterns.

Both the analysis and the support of the creative process are of indirect nature in the here presented framework. The analysis is based on information about the creative process, which is mapped through creativity maps. The results of the analysis are used to stimulate the creativity of the creator by supporting the creative process. Through this, the need to develop techniques for a direct assessment of the creativity can be avoided. Studies about the automatical assessment of creativity have indicated that this process seems to be very complicated, if not impossible. Computational models, focussing on the assessment of creativity itself lack of facilities to properly describe all dimensions of a creative artefact [107].