Towards Bisociative Knowledge Discovery by Michael R. Berthold.
Abstract:
Knowledge discovery generally focuses on finding patterns within a reasonably well connected domain of interest. In this article we outline a framework for the discovery of new connections between domains (so called bisociations), supporting the creative discovery process in a more powerful way. We motivate this approach, show the difference to classical data analysis and conclude by describing a number of different types of domain-crossing connections.
What is a bisociation you ask?
Informally, bisociation can be defined as (sets of) concepts that bridge two otherwise not –or only very sparsely– connected domains whereas an association bridges concepts within a given domain.Of course, not all bisociation candidates are equally interesting and in analogy to how Boden assesses the interestingness of a creative idea as being new, surprising, and valuable [4], a similar measure for interestingness can be specified when the underlying set of domains and their concepts are known.
Berthold describes two forms of bisociation as bridging concepts and graphs, although saying subject identity and associations would be more familiar to topic map users.
This essay introduces more than four hundred pages of papers so there is much more to explore.
These materials are “open access” so take the opportunity to learn more about this developing field.
As always, terminology/identification is going to vary so there will be a role for topic maps.
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