Variable Selection as an Instance-Based Ontology Mapping Strategy

The paper presents a novel instance-based ap- proach to aligning concepts taken from two heterogeneous ontologies populated with text documents. We introduce a concept similarity measure based on the size of the inter- section of the sets of variables which are most important for the class separation of the instances in both input ontologi es. We suggest a VC dimension variable selection criterion elaborated for Support Vector Machines (SVMs), which is novel in the SVMs literature. The study contains results from experiments on real-world text data, where variables are selected using a discriminant analysis framework and standard feature selection techniques for text categoriza tion.