Abstract To bring the Life Sciences domain closer to a Semantic Web realization it is
fundamental to establish meaningful relations between biomedical ontologies. The …

Biomedical ontologies are particularly successful in the uniformization of the life sciences
domain. Due to their recent expansion it became a strenuous task to integrate all the …

BLOOMS is an ontology matching method developed as part of an ontology extension
system for biomedical ontologies. It combines two lexical similarity measures with similarity …

Background We are currently facing a proliferation of heterogeneous biomedical data
sources accessible through various knowledge-based applications. These data are …

Background Biomedical ontologies pose several challenges to ontology matching due both
to the complexity of the biomedical domain and to the characteristics of the ontologies …

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 …

The field of biomedicine has embraced the Semantic Web probably more than any other
field. As a result, there is a large number of biomedical ontologies covering overlapping …

Background Ontologies are commonly used to annotate and help process life sciences data.
Although their original goal is to facilitate integration and interoperability among …

Traditional ontology alignment techniques enable equivalence relationships to be
established between concepts in two ontologies with some confidence value. With semantic …

Automated Semantic Matching of Ontologies with Verification (ASMOV) is a novel algorithm
that uses lexical and structural characteristics of two ontologies to iteratively calculate a …