Using Mappings to Identify Modules. Ontology modularization is an active area of research in the Semantic Web community [3]. With the emergence and wider use of very large ontologies, in particular in fields such as biomedicine, more and more developers need to extract meaningful modules of these ontologies to use in their applications. Researchers have also noted that many ontology-maintenance tasks would be simplified if we could extract modules from ontologies. These tasks include ontology matching: If we can separate ontologies into modules, we can simplify and improve ontology matching. We study a complementary problem: Can we use existing mappings between ontologies to facilitate modularization? Methods. Figure 1 illustrates our method: First, we generate mappings from a source ontology to the target ontology that we wish to modularize. Next, we cluster mappings within the target ontology. Finally, we use mapping clusters to identify modules within an ontology. Validation and Analysis. We validate and analyze our approach by applying our methods to identify modules for NCI Thesaurus [1] and SNOMED-CT [2], two popular and large biomedical ontologies. As domain-specific ontologies for modularization, we used 141 ontologies in BioPortal.1 Our process extracted 71 modules for NCI Thesaurus and 68 modules for SNOMED-CT. We examined modules and their representative terms in order to understand the types of modules that our algorithm creates and to determine whether or not these modules are likely to be useful in an application setting. Figure 2 shows an example, a module of NCI Thesaurus that is relevant to electrocardiograms (EKG) using the Electrocardiography Ontology. The module consists of 61 classes, representative samples of which are shown in the figure. Of the 61 classes in this module, 41 (67%) are mapping targets. Discussion and Conclusions. Our approach uses mappings between ontologies in order to extract domain-specific modules from large ontologies based on their mappings to smaller ontologies. In our experiments with NCI Thesaurus and SNOMED-CT, using the ontologies from BioPortal as the sources for mappings, we have identified a number of useful modules. We found that one of the key hurdles that we must overcome, is to find a way to determine how good a particular module is. Indeed, the same problem is true for most modularization approaches [3]: many authors discuss computational properties of their modules, but do not evaluate how useful these modules are to users. In our case, the requirements for extraction are driven by domain coverage of the module rather than by its computational or structural properties. Thus, the problem of evaluating whether the module satisfies the user requirements is similar to the problem of ontology
evaluation in general: how do we know that an ontology is useful for a specific class of applications? We plan to submit the modules that we have identified to BioPortal to enable the user community to use the modules in their applications, review them and comment on them. However, our initial evidence, which we present in this paper, indicates that our approach can indeed find interesting domain-specific modules.