Abstract
Several authors have suggested similarity measures for objects labeled with terms from a hierarchical taxonomy. We generalize this idea with a definition of information-theoretic similarity for taxonomies that are structured as directed acyclic graphs from which multiple terms may be used to describe an object. We discuss how our definition should be adapted in the presence of ambiguity, and introduce new similarity measures based on our definitions.
We present an implementation of our measures that is integrated with a relational database and scales to large taxonomies and datasets. We evaluate our measures by applying them to an object-matching problem from bioinformatics, and show that, for this task, our new measures outperform those reported in the literature. We also verified the scalability of our approach by applying it to patent similarity search, using patents classified with terms from the taxonomy defined by the United States Patent and Trademark Office.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11914853_71.
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Schwarz, P., Deng, Y., Rice, J.E. (2006). Finding Similar Objects Using a Taxonomy: A Pragmatic Approach. In: Meersman, R., Tari, Z. (eds) On the Move to Meaningful Internet Systems 2006: CoopIS, DOA, GADA, and ODBASE. OTM 2006. Lecture Notes in Computer Science, vol 4275. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11914853_67
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DOI: https://doi.org/10.1007/11914853_67
Publisher Name: Springer, Berlin, Heidelberg
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