Bisociative knowledge discovery for microarray data analysis

Peer reviewe

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

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

Information networks – concept, classification and application

An information network is a structure used for transmitting various forms and types of information. In basic structure, it consists of branches that connect certain nodes. Many scientists and researchers have dealt with the problem of defining an information network, depending on the functional organization and data transmission, and also with the classification of information networks.  Information networks have a very wide application in almost all scientific disciplines. A large number of researches are carried out on the application of information networks (e.g. bisociative, deep information network, heterogeneous information network, and space information network) in the field of medicine for easier detection of diseases, drug development, etc, and other needs to support real-time communication, massive data transmission, and data processing. In accordance with the above mentioned, the aim of this paper is to offer different approaches in defining and classifying general forms of information networks and to notice their wide application in different research disciplines

Stimulating and Simulating Creativity with Dr Inventor

Dr Inventor is a system that is at once, a computational model of creative thinking and also a tool to ignite the creativity process among its users. Dr Inventor uncovers creative bisociations between semi-structured documents like academic papers, patent applications and psychology materials, by adopting a “big data” perspective to discover creative comparisons. The Dr Inventor system is described focusing on the transformation of this textual information into the graph-structure required by the creative cognitive model. Results are described using data from both psychological test materials and published research papers. The operation of Dr Inventor for both focused creativity and open ended creativity is also outlined

Stimulating and Simulating Creativity with Dr Inventor

Dr Inventor is a system that is at once, a computational model of creative thinking and also a tool to ignite the creativity process among its users. Dr Inventor uncovers creative bisociations between semi-structured documents like academic papers, patent applications and psychology materials, by adopting a “big data” perspective to discover creative comparisons. The Dr Inventor system is described focusing on the transformation of this textual information into the graph-structure required by the creative cognitive model. Results are described using data from both psychological test materials and published research papers. The operation of Dr Inventor for both focused creativity and open ended creativity is also outlined

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