Creative Exploration Using Topic Based Bisociative Networks

Partial funding for Open Access provided by the UMD Libraries' Open Access Publishing Fund.Bisociative knowledge discovery is an approach that combines elements from two or more `incompatible' domains to generate creative solutions and insight. Inspired by Koestler's notion of bisociation, in this paper we propose a computational framework for the discovery of new connections between domains to promote creative discovery and inspiration in design. Specifically, we propose using topic models on a large collection of unstructured text ideas from multiple domains to discover creative sources of inspiration. We use these topics to generate a Bisociative Information Network - a graph that captures conceptual similarity between ideas - that helps designers find creative links within that network. Using a dataset of thousands of ideas from OpenIDEO, an online collaborative community, our results show usefulness of representing conceptual bridges through collections of words (topics) in finding cross-domain inspiration. We show that the discovered links between domains, whether presented on their own or via ideas they inspired, are perceived to be more novel and can also be used as creative stimuli for new idea generation

Towards creative information exploration based on Koestler's concept of bisociation

Creative information exploration refers to a novel framework for exploring large volumes of heterogeneous information. In particular, creative information exploration seeks to discover new, surprising and valuable relationships in data that would not be revealed by conventional information retrieval, data mining and data analysis technologies. While our approach is inspired by work in the field of computational creativity, we are particularly interested in a model of creativity proposed by Arthur Koestler in the 1960s. Koestler’s model of creativity rests on the concept of bisociation. Bisociative thinking occurs when a problem, idea, event or situation is perceived simultaneously in two or more “matrices of thought” or domains. When two matrices of thought interact with each other, the result is either their fusion in a novel intellectual synthesis or their confrontation in a new aesthetic experience. This article discusses some of the foundational issues of computational creativity and bisociation in the context of creative information exploration

A Bisociated Domain-Based Serendipitous Novelty-Recommendation Technique for Recommender Systems

Traditional recommendation paradigms such as content-based filtering (CBF) tend to recommend items that are very similar to user profile characteristics and item input, resulting in the classical twin problem of overspecialization and concentration bias of recommendations. This twin problem is prevalent with CBF recommender systems due to the utilisation of accuracy metrics to retrieve similar items, and, limiting recommendation computations to single recognized user-centered domains, rather than cross-domains.  This paper proposes a Bisociated domain-based serendipitous novelty recommendation techniques using Bisolinkers exploratory creativity discovery technique. The use of Bisolinkers enables establishing unique links between two seemingly unrelated domains, to enhance recommendation accuracy and user satisfaction. The presence of similar terms in two habitually incompatible domains demonstrates that two seemingly unrelated domains contain elements that are related and may act as a link to connect these two domains. Keywords: recommender systems, novelty, machine learning, outlier detection, bisociation &nbsp

Discovery of Novel Term Associations in a Document Collection

Non peer reviewe

Applying blended conceptual spaces to variable choice and aesthetics in data visualisation

Computational creativity is an active area of research within the artificial intelligence domain that investigates what aspects of computing can be considered as an analogue to the human creative process. Computers can be programmed to emulate the type of things that the human mind can. Artificial creativity is worthy of study for two reasons. Firstly, it can help in understanding human creativity and secondly it can help with the design of computer programs that appear to be creative. Although the implementation of creativity in computer algorithms is an active field, much of the research fails to specify which of the known theories of creativity it is aligning with. The combination of computational creativity with computer generated visualisations has the potential to produce visualisations that are context sensitive with respect to the data and could solve some of the current automation problems that computers experience. In addition theories of creativity could theoretically compute unusual data combinations, or introducing graphical elements that draw attention to the patterns in the data. More could be learned about the creativity involved as humans go about the task of generating a visualisation. The purpose of this dissertation was to develop a computer program that can automate the generation of a visualisation, for a suitably chosen visualisation type over a small domain of knowledge, using a subset of the computational creativity criteria, in order to try and explore the effects of the introduction of conceptual blending techniques. The problem is that existing computer programs that generate visualisations are lacking the creativity, intuition, background information, and visual perception that enable a human to decide what aspects of the visualisation will expose patterns that are useful to the consumer of the visualisation. The main research question that guided this dissertation was, “How can criteria derived from theories of creativity be used in the generation of visualisations?”. In order to answer this question an analysis was done to determine which creativity theories and artificial intelligence techniques could potentially be used to implement the theories in the context of those relevant to computer generated visualisations. Measurable attributes and criteria that were sufficient for an algorithm that claims to model creativity were explored. The parts of the visualisation pipeline were identified and the aspects of visualisation generation that humans are better at than computers was explored. Themes that emerged in both the computational creativity and the visualisation literature were highlighted. Finally a prototype was built that started to investigate the use of computational creativity methods in the ‘variable choice’, and ‘aesthetics’ stages of the data visualisation pipeline.School of ComputingM. Sc. (Computing

Conceptual Representations for Computational Concept Creation

Computational creativity seeks to understand computational mechanisms that can be characterized as creative. The creation of new concepts is a central challenge for any creative system. In this article, we outline different approaches to computational concept creation and then review conceptual representations relevant to concept creation, and therefore to computational creativity. The conceptual representations are organized in accordance with two important perspectives on the distinctions between them. One distinction is between symbolic, spatial and connectionist representations. The other is between descriptive and procedural representations. Additionally, conceptual representations used in particular creative domains, such as language, music, image and emotion, are reviewed separately. For every representation reviewed, we cover the inference it affords, the computational means of building it, and its application in concept creation.Peer reviewe

(Missing) Concept Discovery in Heterogeneous Information Networks

This article proposes a new approach to extract existing (or detect missing) concepts from a loosely integrated collection of information units by means of concept graph detection. Thereby a concept graph defines a concept by a quasi bipartite sub-graph of a bigger network with the members of the concept as the first vertex partition and their shared aspects as the second vertex partition. Once the concepts have been extracted they can be used to create higher level representations of the data. Concept graphs further allow the discovery of missing concepts, which could lead to new insights by connecting seemingly unrelated information units

Further dimensions: text, typography and play in the metaverse

In this text I wish to delve into the creation of textual content as well as its visualization through typographic design mechanisms inside three dimensional virtual worlds, which are known as the metaverse. I am particularly focused upon the way in which such virtually three dimensional environments may place the usage of text within a context that stands in contradiction to its traditional one by creating an unexpected novel purpose which takes a marked departure from the intrinsic attribute with which text has inherently been associated – namely the attribute of readability. In such environments readability, or indeed even legibility, may often be displaced through the usage of text and typography as a playful device, as artifacts which may manifest in puzzle-like configurations, or as visual structures the contents of which are meant to be understood through means other than straightforward reading; thus bringing about states of heightened engagement, wonder and ‘play’ through their manipulation or indeed simply by being immersed within the spaces which are brought about through their very agency. I also wish to expand upon this subject by talking about my own experiments with this material and will conclude by positing that further virtual dimensions can be instrumental in eliciting exciting alternative usages of text and typography which bring to the fore the allographic properties of text as an artistic/creative expressive media that may well bear further scrutiny and exploration

Effects of data exploration and use of data mining tools to extract knowledge from databases (KDD) in early stages of the Engineering design process (EDP)

This thesis describes original research work where the objective was to provide teams with access to data, and observe the effect of its use at the early creative stages of the engineering design process. Following a theoretical research on the use of information technologies to support idea generation, and the use of data as creative input, a procedure was designed following the Knowledge Discovery from Databases process, and tried over several iterations of improvement working with creative teams in different contexts. After two exploratory studies, three cases were performed where the researcher attempted to better support the different stages of the EDP through the application of data from patent mining. To observe the differences, we provided three levels of access to explore data in a data mining tool: low, intermediate and high. • Case 1 - Participants in a creativity session were asked to identify needs or problems (first stage of the engineering design process). They were given intermediate access to explore data in a data mining tool, meaning they could explore, but not make new searches or add data. The analysis of the results indicates that participants gravitated towards terms and keywords related to previously generated ideas, thus the increase in novelty was low. In order to correct the issue of intermediate exploration, it was decided to train participants in the use of the data mining tool for subsequent cases; if teams have more freedom to explore data, they can potentially generate more novel combinations. • Case 2 - Teams tasked with engineering challenges in a course were trained in the use of the data exploration tool. They were then invited to continue using the tool to generate new ideas. In this case, teams had high access to the data exploration tool; they were able to add data, and make searches. Teams who chose to explore data for creative support found improvements or components from existing solutions to advance their own design, and received more positive evaluations by a jury of experts. However, the objective of obtaining more diverse or novel solutions was not achieved. A possible explanation is that the use of the tool can overwhelm participants with too many options to explore, leading teams to return to known solutions. A possible counteraction to resolve the issue of too many options is to have an external actor (such as a moderator) extract keywords from the data, and provide participants with these terms to combine into novel ideas. • Case 3 - Teams participating in an innovation contest were given keywords selected by an expert on the tool. In other words, participants had low access to explore data in a data mining tool. The researcher performed the data analysis for two challenges in the competition, and selected keywords relevant to the knowledge base of the problem. The results show that teams who selected the keyword supported challenges generated more diverse and novel ideas, compared to teams without the support. By providing relevant keywords, it was possible to obtain the benefits of the KDD without the issues of training participants on the use of the tool, and the resources teams would have to dedicate to explore the data. It was concluded that data and KDD can be used as a creative input for an EDP at different stages. It is recommended to determine whether the objective of including data in an EDP effort is to generate a novel idea or to solve a problem. To generate novel ideas, it seems preferable to provide data in the form of keywords selected by an external actor, to prompt original combinations. If the team is searching for incremental improvements or elements of existing solutions, then it appears to be beneficial to have access to a knowledge base to explore. It is important to delimit the exploration to avoid becoming stunned because of the amount of available information. For the three experiences, the software IPMetrix was used to perform the data mining. The process of data selection, loading, cleaning and transformation is described in each chapter, according to the work performed on the data for the specific case