Creating mappings for ontologies in biomedicine: simple methods work

Abstract

Creating mappings between concepts in different ontologies is a critical step in facilitating data integration. In recent years, researchers have developed many elaborate algorithms that use graph structure, background knowledge, machine learning and other techniques to generate mappings between ontologies. We compared the performance of these advanced algorithms on creating mappings for biomedical ontologies with the performance of a simple mapping algorithm that relies on lexical matching. Our evaluation has shown that (1) most of the advanced algorithms are either not publicly available or do not scale to the size of biomedical ontologies today, and (2) for many biomedical ontologies, simple lexical matching methods outperform most of the advanced algorithms in both precision and recall. Our results have practical implications for biomedical researchers who need to create alignments for their ontologies.

Figure 1.

Performance of LOOM and OAEI tools in the Anatomy track (Experiment 1). The graph shows the results for OAEI 2008 from the competition report along with LOOM results on the same set, ordered by the F-measure. The tools produced mappings for the Mouse adult gross anatomy ontology and the human anatomy part of the NCI Thesaurus. A reference alignment was used as the basis for precision and recall.

Figure 2

Example of incorrect mapping produced by lexical matching. The synonym of a class in NCI Thesaurus matches the preferred name for a class in Mouse adult gross anatomy. However, this match is misleading because there is a more precise match for the class Respiratory system.