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Finding Similar Objects Using a Taxonomy: A Pragmatic Approach
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Ontologies, Databases and Applications of Semantics (ODBASE) 2006 International Conference
Similarity and Matching
Finding Similar Objects Using a Taxonomy: A Pragmatic Approach
Peter Schwarz1  , Yu Deng2 and Julia E. Rice1
| (1) |
IBM Almaden Research Center, San Jose, CA 95120, |
| (2) |
IBM Thomas J. Watson Research Center, Yorktown Heights, NY 10598, |
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.
Keywords: Semantic similarity measures, Object matching, Taxonomy, Information theoretic similarity.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11914853_71.
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