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
Discovering temporal bisociations for linking concepts over time
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Time-based discovery in biomedical literature: mining temporal links
International Journal of Data Analysis Techniques and Strategies
| Hi-index | 0.00 |
According to Koestler, the notion of a bisociation denotes a connection between pieces of information from habitually separated domains or categories. In this paper, we consider a methodology to find such bisociations using a network representation of knowledge, which is called a BisoNet, because it promises to contain bisociations. In a first step, we consider how to create BisoNets from several textual 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. Future work includes extending our methods to non-textual data, improving the similarity measure, and applying more sophisticated graph mining methods.