  Motivation: Entropy measurements on hierarchical structures have been used in
methods for information retrieval and natural language modeling. Here we
explore its application to semantic similarity. By finding shared ontology
terms, semantic similarity can be established between annotated genes. A common
procedure for establishing semantic similarity is to calculate the
descriptiveness (information content) of ontology terms and use these values to
determine the similarity of annotations. Most often information content is
calculated for an ontology term by analyzing its frequency in an annotation
corpus. The inherent problems in using these values to model functional
similarity motivates our work. Summary: We present a novel calculation for
establishing the entropy of a DAG-based ontology, which can be used in an
alternative method for establishing the information content of its terms. We
also compare our IC metric to two others using semantic and sequence
similarity.
