An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
A transductive framework of distance metric learning by spectral dimensionality reduction
Proceedings of the 24th international conference on Machine learning
Clustering for metric and non-metric distance measures
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
Content-based ontology matching for GIS datasets
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
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Ontological structures provide a rich hierarchy of concepts and relationships that are helpful in exploratory analysis. Ontologies, however, are often categorical, which introduces ambiguity, and makes numerical analysis difficult. Adding to the problem is the fact that as the number of ontological concepts increases so does computational complexity for a variety of analytical tasks. In this paper, we propose both spatial and ontological co-occurrence as a means to derive similarity among categorical values. More specifically, we devise a method that combines entity location as well as categorical frequency into a numerical measure of similarity for any pair of categorical values. In addition, we show how different ontological levels can hide or uncover information content while influencing the number of processed categorical values. We provide experiments that demonstrate the effectiveness of our approach.