Ontologies should play a critical role in the achievement of semantic interoperability in healthcare. However, many of the available biomedical ontologies are rich in human understandable labels, but are less rich in machine processable axioms, so their effectiveness for supporting advanced data analysis processes is limited. In previous works, we have presented a method for the analysis of the lexical regularities in the labels of the classes of biomedical ontologies, including finding matches in other ontologies. This method has been able to find that the structure and content of many biomedical ontologies present a degree of regularity that would make them suitable for the application of automatic axiomatic enrichment processes. The Gene Ontology (GO) is an example of an ontology that is widely used for functional annotation of genes and proteins and whose labels present a high degree of regularity. Recently, the GO Consortium enriched the ontology by using the so-called cross-products extensions (CPE). Cross-products are generated by establishing axioms that relate a given GO class with classes from GO or other biomedical ontologies. In this paper, we study how our lexical analysis method is able to identify and reconstruct the cross-products defined by the Gene Ontology Consortium, in order to gain new knowledge about the potential and usefulness of our approach as well as the requirements of cross-products efforts, since we would be interested in applying a similar approach to enrich other biomedical ontologies.

What could you find in this web page?

In this web page you could find the files with information about the lexical analysis and the calculation of the CPE. We have used this information to create the tables and figures used in the paper.