Bisociation Networks for Creative Information Discovery (BISON)

The concept of association is at the heart of many of today's powerful ICT technologies such as information retrieval and data mining. These technologies typically employ association by similarity or co-occurrence to discover new information relevant to the evidence already known to the user. However, association techniques fail to discover relevant information that is not related in obvious associative ways, in particular information that is related across different contexts. It is these kinds of context-crossing associations that are often needed in innovative domains.

Domains that are characterized by the need to develop innovative solutions require a form of creative information discovery from increasingly complex, heterogeneous and geographically distributed information sources. These domains, including design and engineering (drugs, materials, processes, devices), areas involving art (fashion and entertainment), and scientific discovery disciplines, require a different ICT paradigm that can help users to uncover, select, re-shuffle, and combine diverse contents to synthesize new features and properties leading to creative solutions. People working in these areas employ creative thinking to connect seemingly unrelated information, for example, by using metaphors or analogical reasoning. These modes of thinking allow the mixing of conceptual categories and contexts, which are normally separated. The functional basis for these modes is a mechanism called bisociation.

The main goal of the project is to develop a system(BISON) that makes use of these bisociation mechanisms. We anticipate that the BISON system will provide truly creative solutions in an interactive environment that implements novel knowledge integration, network visualisation and machine learning methods to aid creative discovery. BISON builds on widely researched methodologies such as association rule learning, analogical, metaphoric and case-based reasoning.

The concept of association is at the heart of many of today's powerful ICT technologies such as information retrieval and data mining. These technologies typically employ association by similarity or co-occurrence to discover new information relevant to the evidence already known to the user. However, association techniques fail to discover relevant information that is not related in obvious associative ways, in particular information that is related across different contexts. It is these kinds of context-crossing associations that are often needed in innovative domains.

Domains that are characterized by the need to develop innovative solutions require a form of creative information discovery from increasingly complex, heterogeneous and geographically distributed information sources. These domains, including design and engineering (drugs, materials, processes, devices), areas involving art (fashion and entertainment), and scientific discovery disciplines, require a different ICT paradigm that can help users to uncover, select, re-shuffle, and combine diverse contents to synthesize new features and properties leading to creative solutions. People working in these areas employ creative thinking to connect seemingly unrelated information, for example, by using metaphors or analogical reasoning. These modes of thinking allow the mixing of conceptual categories and contexts, which are normally separated. The functional basis for these modes is a mechanism called bisociation.

The main goal of the project is to develop a system(BISON) that makes use of these bisociation mechanisms. We anticipate that the BISON system will provide truly creative solutions in an interactive environment that implements novel knowledge integration, network visualisation and machine learning methods to aid creative discovery. BISON builds on widely researched methodologies such as association rule learning, analogical, metaphoric and case-based reasoning.