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 AgreementMakerLight ontology matching framework, and evaluate it using the benchmark biomedical ontology matching tasks from the Ontology Alignment Evaluation Initiative (OAEI) 2013. In each matching problem, our methodology consistently identified the sources of background knowledge that led to the highest improvements over the baseline alignment (i.e., without background knowledge). Furthermore, our proposed mapping gain parameter is strongly correlated with the F-measure of the produced alignments, thus making it a good estimator for ontology matching techniques based on background knowledge.
Highlights
Ontology matching is a task of critical importance in the context of the semantic web that has applications in fields such as ontology engineering and information integration [1,2,3,4,5,6,7,8,9]
Correlation with F-measure As we detailed in the Methods section, the mapping gain measures the fraction of indirect mappings derived from a background knowledge source over the direct mappings
Erroneous matches are more likely in the whole ontologies matching tasks, which means that precision can vary more between background knowledge sources, leading to deviations from the linear behavior and to lower correlation coefficients
Summary
Ontology matching is a task of critical importance in the context of the semantic web that has applications in fields such as ontology engineering and information integration [1,2,3,4,5,6,7,8,9] It has gained particular relevance in the life sciences domain due to the prominent role ontologies have taken in representing knowledge in this domain [10,11]. In addition to these ontologies, we used a portion of the UMLS Metathesaurus [30] as a multi-domain biomedical background knowledge source. We studied the effect of the mapping gain threshold on the quality of the results in order to identify a suitable threshold for our methodology
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