Automatic background knowledge selection for matching biomedical ontologies.

ABSTRACT: Ontology matching is a growing field of research that is of critical importance for the semantic web initiative. The use of background knowledge for ontology matching is often a key factor for success, particularly in complex and lexically rich domains such as the life sciences. However, in most ontology matching systems, the background knowledge sources are either predefined by the system or have to be provided by the user. In this paper, we present a novel methodology for automatically selecting background knowledge sources for any given ontologies to match. This methodology measures the usefulness of each background knowledge source by assessing the fraction of classes mapped through it over those mapped directly, which we call the mapping gain. We implemented this methodology in the Ag

SUBMITTER: Faria D 

PROVIDER: S-EPMC4224389 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature