Support: A Prototype to Support Divergent Thinking. Dominik Siemon and Susanne Robra-Bissantz. Chair Information Management, University of Braunschweig, ...
Integration of Information Retrieval in Creativity Support: A Prototype to Support Divergent Thinking Dominik Siemon and Susanne Robra-Bissantz Chair Information Management, University of Braunschweig, Germany {d.siemon,s.robra-bissantz}@tu-bs.de
Abstract. Creativity support has been adressed in various fields of studies and already entered information technology with the development of different tools in order to enable, foster and improve the creative stage. In this paper we outline our approach of supporting the divergent thinking process during the generative stage by using information extraction and information retrieval methods as well as social media for the actual supporting content. A prototype that offers an automated support and that tries to produce a broad benefit for the idea generator was developed and will further be evaluated according to current design science guidelines. Keywords: Creativity support system, divergent thinking, information retrieval.
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Introduction and Design Theory
Both, creativity and the generation of ideas are processes, which can arise autonomous and independent from the environment. However supporting this processes can enhance the idea itself. This creativity support can be done in traditional ways or with the help of information technology by specific software tools, interactive interfaces or rich visualization- and searching tools [1]. Usually these tools support the recording and management of ideas, rather than supporting the actual generative creativity process [1]. This generative process describes the production of ideas during the stage of creative thinking [2,3]. Creative thinking is defined as the capability of an individual to form associations, develop patterns and possess a certain degree of abstraction, which is essentially based on a divergent reasoning process as developed by the psychologist J.P. Guilford [4]. This divergent thinking offers a subjective consideration, enabling mental leaps [5] and breaking away from familiar structures, in order to generate new patterns and solutions [4]. In our research we concentrate on the support of the generative process by the aid of our prototype, which facilitates divergent thinking. The prototype is designed to support engineers, scientists, product managers or artists to make new discoveries and evaluate ideas or to support problem-solving processes. A common method for supporting the creative thinking process is to explore and search for inspiration, which has already been integrated into different IS, such as search engines [1]. The underlying principle of these techniques is, that the individual generating the idea is aware of the actual support he is seeking [6]. An individual seeking for inspiration with the help of a search engines is aware of the M.C. Tremblay et al. (Eds.): DESRIST 2014, LNCS 8463, pp. 388–392, 2014. © Springer International Publishing Switzerland 2014
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terms he is using for his search. Therefore an additional benefit, which generates new patterns and finds unknown issues, is not necessarily provided [6, 7]. Possible reliefs for this problem might be specific search engines, especially made for the generation of associations and patterns [6]. Nevertheless, the individual is always aware of the initial query (terms) he is using for seeking support [6, 7]. In our approach we attempt to avoid this awareness by using information extraction (IE) to analyze written ideas and information retrieval (IR) methods to query data sources to support the creative process of the idea generator.
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Design of the Artifact
2.1
Information Retrieval for Creativity Support
IE is the process of extracting and obtaining data from given resources i.e. from a document, which contains full-text or other data. IE algorithms try to reduce documents to a more dense and short representation. Furthermore IR selects a set of documents based upon a given query [8]. This is why IE systems work essentially by means of convergent information processing [9]. Analyzing the given idea will thus not support the divergent thinking process, because it simply extracts the basic information from the idea. No new patterns or solutions can be found upon these analyses [9]. Therefore new issues must be evaluated rest upon the results of the analyses to support the actual divergent thinking process. This process takes part in the next step, where specific data sources will be queried for potential supportive information. This information can therefore stimulate inspiration, emerge new association or empower users to be more creative [7], [9]. The main difference is thus not the active search for creativity support by the idea generator but the automated process by the system, which can expand the information horizon of the idea generator and support the actual creativity process [6]. For this to work effectively the IR must operate precisely and a data source must be selected that provides potential associations and inspiration. The following chapter explains this approach and the developed prototype in more detail and shows the technical capabilities of the artifact [10]. A reasonable data source is difficult to determine, as it depends on the manner of ideas being generated. An idea generation process has usually a specific topic and a goal to solve certain problems or to achieve specific tasks. Thus all ideas can be categorized before the support can take affect and specific data sources can be determined. However data sources shouldn’t rely on topic restrictions, because supporting content can also be found in unrelated data sources [6], [9]. 2.2
Implementation
In the course of this research, a web-based prototype for supporting the generation of ideas was developed. The prototype captures written ideas (see Fig. 1, S1) and analyses them with a designed IE algorithm (see Fig. 1, S2). This IE algorithm is based upon the bag-of-words model1, representing the idea in a predefined amount of valued words (list), which will be used to query designated data sources. 1
The bag-of-words model is a document representation, where a text (document) is represented by an ordered set (bag) of words by their frequency of occurrence in the text.
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This list is ordered by the appearance of a word with a weightage relative to the number of all valued words (words without stop words). The outcome of this algorithm is a ranked list of valued terms, which represents the written idea (see Fig. 1, S2). In a set of combinations, these valued terms will then be used to query several data sources (see Fig. 1, S3).
Fig. 1. Functionality of the prototype
Different data sources can obtain variable types of information and can thus support the creativity process in diverse ways. Special databases applied for different fields of problems and information can be used to find creativity supporting content. In our approach we defined social media applications as data sources. Social media refers to applications, where users interact in social networks and create and share content. The popularity of social media applications is immense, which results in a big amount of data created by users all over the globe. The democratization of knowledge and information supports the transformation of the user from a mere (information) consumer to a producer. The widespread access and the high usage of social media turns it into a data source with a wide range of topics, that is not restricted to specific users [11]. Our approach queries the services Facebook, Twitter and Tumblr via the API’s and their REST2 architectural hypermedia data system. All queries aim for the main text or message written by the users, e.g. the tweets, messages and posts. The results are saved into a valued list and are presented to the idea generator inside the web application, offering to read the messages, delete unrelated results or mark them as important (see Fig. 1, S4). The following screenshot (Fig. 2) shows the presentation view with the IR results, arranged by their relevance towards the idea.
2
REST stands for Representational State Transfer and describes a concept of resource access for Web applications.
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Fig. 2. Presentation view with the IR results
General IR algorithms underlie a strict effectiveness on how the results obtain a certain quality towards the query. This quality can be defined by the relevance theory, identified by Saracevic, which explains how well a retrieved document meets the query [12]. According to the nature of creativity supporting content, these general relevance restrictions can be neglected [6], [9].
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Evaluation of the Artifact and Future Research
We aim to evaluate the utility, quality and efficacy of the artifact [10] by defining a prior aspect in this research. This aspect is the theory in what way the IE algorithm can represent the idea in a respectful way. To evaluate this, subjects will be asked to write down ideas, which will be analyzed by the IE algorithm. The extracted results will be presented to the idea generators and feedback on how well the IE worked will be collected. After that, the main hypothesis will be evaluated with two focal groups. One group will be assisted by the prototype whereas the other group will receive no assistance. Experts will give the two groups the same tasks or problems to solve over a specific time period. The experts will then evaluate the ideas according to their quality [13]. Assessing and evaluating the quality of ideas, especially by influencing and supporting the creative stage, is difficult and complex. Terasa M. Amabile defined an approach on how to measure creativity by setting special dimensions [14]. The experts, who issued the tasks and problems to the subjects, will use this approach to assess the ideas. In addition to that, the subjects will be asked if the information found by the algorithm was able to support the idea generation stage.
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