Evolution of biomedical ontologies and mappings: Overview of recent approaches <s> Evolution of merged ontologies <s> We present Uberon, an integrated cross-species ontology consisting of over 6,500 classes representing a variety of anatomical entities, organized according to traditional anatomical classification criteria. The ontology represents structures in a species-neutral way and includes extensive associations to existing species-centric anatomical ontologies, allowing integration of model organism and human data. Uberon provides a necessary bridge between anatomical structures in different taxa for cross-species inference. It uses novel methods for representing taxonomic variation, and has proved to be essential for translational phenotype analyses. Uberon is available at http://uberon.org <s> BIB001 </s> Evolution of biomedical ontologies and mappings: Overview of recent approaches <s> Evolution of merged ontologies <s> The proliferation of ontologies and taxonomies in many domains increasingly demands the integration of multiple such ontologies. We propose a new taxonomy merging algorithm called Atom that, given as input two taxonomies and a match mapping between them, can generate an integrated taxonomy in a largely automatic manner. The approach is target-driven, i.e. we merge a source taxonomy into the target taxonomy and preserve the target ontology as much as possible. In contrast to previous approaches, Atom does not aim at fully preserving all input concepts and relationships but strives to reduce the semantic heterogeneity of the merge results for improved understandability. Atom can also exploit advanced match mappings containing is-a relationships in addition to equivalence relationships between concepts of the input taxonomies. We evaluate Atom for synthetic and real-world scenarios and compare it with a full merge solution. <s> BIB002
The existing approaches only consider the migration of ontologybased mappings between two different sources. However, there are more complex scenarios where a holistic view with more than two sources is needed. For instance, there are efforts to merge several biomedical ontologies in order to provide one integrated ontology in a domain of interest. Beside the huge UMLS there are other merged ontologies such as Uberon BIB001 in the anatomical domain. Such merge processes are currently done largely manually and thus tedious and error-prone. Furthermore, they cannot easily deal with the continuous changes of the underlying source ontologies. A more automated approach would be to utilize mappings between the source ontologies and merged target ontology and apply a mapping-based merge algorithm BIB002 . For new versions of the source ontologies, the ontology mappings and merge result need to be adapted. Such an evolutionaware merge approach is challenging and involves several interrelated subproblems. 1. The source ontologies evolve based on their requirements to incorporate new knowledge and follow their design guidelines. 2. This has impact on the mappings between the source ontologies (e.g. SNOMED CT, NCIT) and the integrated target ontology (e.g. UMLS), i.e. those mappings need to be adapted accordingly. 3. The integrated ontology follows its own design guidelines that can result into changes independent from the source ontologies. 4. The integrated ontology needs to be adapted accordingly by taking the source ontology and mapping changes as well as its own changes into account. During this process existing curated knowledge must not disappear, e.g., a change in the integrated ontology should not be overwritten during an update process. However, the evolution in the source ontologies should still be reflected appropriately in a merged ontology. The described scenario shows that those inter-dependencies between several sources as well as changes in the individual sources require more advanced data and ontology migration approaches.