| Abstract: | Computation of semantic similarity between concepts is a very common problem
in many language related tasks and knowledge domains. In the biomedical field, several approaches have been developed to deal with this issue by exploiting the structured knowledge
available in domain ontologies (such as SNOMED-CT or MeSH) and specific, closed and
reliable corpora (such as clinical data). However, in recent years, the enormous growth of
the Web has motivated researchers to start using it as the corpus to assist semantic analysis
of language. This paper proposes and evaluates the use of the Web as background corpus for
measuring the similarity of biomedical concepts. Several ontology-based similarity measures
have been studied and tested, using a benchmark composed by biomedical terms, comparing
the results obtained when applying them to the Web against approaches in which specific
clinical data were used. Results show that the similarity values obtained from the Web for
ontology-based measures are at least and even more reliable than those obtained from specific
clinical data, showing the suitability of the Web as information corpus for the biomedical
domain. |
