1. Field of Invention
The present invention relates generally to measures of similarity. More specifically, the present invention is related to computer-based method to find similar objects using a taxonomy.
2. Discussion of Prior Art
Taxonomies have long been recognized as a useful tool for classification. In addition to providing a precise way to name classes of individuals that share certain properties or behavior, they also provide a means of determining how similar one such individual is to another. In its simplest form, a taxonomy defines a hierarchical grouping of individuals into ever more specific classes. Two individuals share the properties of the most specific grouping that includes both of them, and the degree to which the two individuals are similar depends on the location of this class in the hierarchy. The lower in the hierarchy, the more similar the individuals are. Thus, for example, two rats of the same species are more similar than rats of different species, and a rat of any species is more similar to another rodent than it is to a camel. Various authors (see papers to: (1) Resnik titled “Using information content to evaluate semantic similarity in a taxonomy”, (2) Wu et al. titled “Verb semantics and lexical selection”, and (3) Lin titled “An information-theoretic definition of similarity”) have defined ways of turning this intuitive idea of similarity into a numeric value that can be used to rank the similarity of objects.
The ability to find similar objects given a description of a target is useful in many domains. For example, one may wish to find patents similar to a given patent, or subjects similar to a hypothetical “ideal” subject for a clinical trial. In bioinformatics, one may wish to find gene products (e.g. proteins) similar to a given gene product. In each of these domains, and others, comprehensive taxonomies have been defined and used by various organizations to classify sets of objects.
Classification using such taxonomies is more complex than the simple example described above. Firstly, it is frequently the case that a class of individuals may specialize the properties of more than one parent class. Furthermore, taxonomies often evolve, as new specialized groupings are formed and older ones are reorganized. Even with an unchanging taxonomy, the classification of a particular object may evolve as more is learned about it or users of the taxonomy may disagree as to how it should be classified. Lastly, real taxonomies tend to be quite large, and the sets of objects they are used to classify are often very large. Thus, any approach to finding similar, objects must scale well in both these dimensions.
Whatever the precise merits, features, and advantages of the above cited similarity measures, none of them achieves or fulfills the purposes of the present invention.