The structure-mapping engine: algorithm and examples
Artificial Intelligence
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Journal of Experimental & Theoretical Artificial Intelligence
An architecture for hybrid creative reasoning
Soft computing in case based reasoning
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Journal of the American Society for Information Science and Technology
Computation for metaphors, analogy, and agents
Bisociative Knowledge Discovery
Discovery of novel term associations in a document collection
Bisociative Knowledge Discovery
Bisociative music discovery and recommendation
Bisociative Knowledge Discovery
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According to Koestler, the notion of a bisociation denotes a connection between pieces of information from habitually separated domains or categories. In this chapter, we consider a methodology to find such bisociations using a BisoNet as a representation of knowledge. In a first step, we consider how to create BisoNets from several tex- tual databases taken from different domains using simple text-mining techniques. To achieve this, we introduce a procedure to link nodes of a BisoNet and to endow such links with weights, which is based on a new measure for comparing text frequency vectors. In a second step, we try to rediscover known bisociations, which were originally found by a human domain expert, namely indirect relations between migraine and magnesium as they are hidden in medical research articles published before 1987. We observe that these bisociations are easily rediscovered by simply following the strongest links.