Evolution of biomedical ontologies and mappings: Overview of recent approaches <s> Change detection <s> As ontology development becomes a more ubiquitous and collaborative process, the developers face the problem of maintaining versions of ontologies akin to maintaining versions of software code in large software projects. Versioning systems for software code provide mechanisms for tracking versions, checking out versions for editing, comparing different versions, and so on. We can directly reuse many of these mechanisms for ontology versioning. However, version comparison for code is based on comparing text files--an approach that does not work for comparing ontologies. Two ontologies can be identical but have different text representation. We have developed the PROMPTDIFF algorithm, which integrates different heuristic matchers for comparing ontology versions. We combine these matchers in a fixed-point manner, using the results of one matcher as an input for others until the matchers produce no more changes. The current implementation includes ten matchers but the approach is easily extendable to an arbitrary number of matchers. Our evaluation showed that PROMPTDIFF correctly identified 96% of the matches in ontology versions from large projects. <s> BIB001 </s> Evolution of biomedical ontologies and mappings: Overview of recent approaches <s> Change detection <s> Evolutionary Terminology Auditing (ETA) is a novel way to assess the quality of terminologies using reality as benchmark. The key idea is that terms added to each new version of a terminology reflect unjustified absences and terms that are deleted unjustified presences in previous versions of the terminology. The method requires that terminology authors not only keep track of changes in successive versions, but also motivate the changes introduced. In this paper, we report on how our method has been applied to the Gene Ontology (GO), a collection of three structured, controlled vocabularies for use in annotating genes, gene products and sequences. We demonstrate that even where the basic requirements for its application are only partially satisfied, the approach can still yield results which are useful for quantifying and forecasting the evolution of a terminology's quality over time. <s> BIB002 </s> Evolution of biomedical ontologies and mappings: Overview of recent approaches <s> Change detection <s> Evolutionary Terminology Auditing is a technique designed to measure quality improvements of terminologies over successive versions. It uses the most recent version of a terminology as a benchmark and assumes that changes in the underlying ontology correspond to changes in either that part of reality that is covered by the terminology, or the authors’ understanding – if not the ‘state of the art’ in general – thereof. Applied to SNOMED CT over 18 versions, it reveals that at the level of the concepts minimal improvements are obtained and that the second assumption holds for far less changes than one would expect. It is recommended that future versions of SNOMED CT provide more explicit documentation for each introduced change. <s> BIB003 </s> Evolution of biomedical ontologies and mappings: Overview of recent approaches <s> Change detection <s> Life science ontologies evolve frequently to meet new requirements or to better reflect the current domain knowledge. The development and adaptation of large and complex ontologies is typically performed collaboratively by several curators. To effectively manage the evolution of ontologies it is essential to identify the difference (Diff) between ontology versions. Such a Diff supports the synchronization of changes in collaborative curation, the adaptation of dependent data such as annotations, and ontology version management. We propose a novel approach COnto-Diff to determine an expressive and invertible diff evolution mapping between given versions of an ontology. Our approach first matches the ontology versions and determines an initial evolution mapping consisting of basic change operations (insert/update/delete). To semantically enrich the evolution mapping we adopt a rule-based approach to transform the basic change operations into a smaller set of more complex change operations, such as merge, split, or changes of entire subgraphs. The proposed algorithm is customizable in different ways to meet the requirements of diverse ontologies and application scenarios. We evaluate the proposed approach for large life science ontologies including the Gene Ontology and the NCI Thesaurus and compare it with PromptDiff. We further show how the Diff results can be used for version management and annotation migration in collaborative curation. <s> BIB004 </s> Evolution of biomedical ontologies and mappings: Overview of recent approaches <s> Change detection <s> Changes in an upper level ontology have obvious consequences for the domain ontologies that use it at lower levels. It is therefore crucial to document the changes made between successive versions of ontologies of this kind. We describe and apply a method for tracking, explaining and measuring changes between successive versions of upper level ontologies such as the Basic Formal Ontology (BFO). The proposed change-tracking method extends earlier work on Realism- Based Ontology Versioning (RBOV) and Evolutionary Terminology Auditing (ETA). We describe here the application of this evaluation method to changes between BFO 1.0, BFO 1.1, and BFO 2.0. We discuss the issues raised by this application and describe the extensions which we added to the original evaluation schema in order to account for changes in this type of ontology. The results of our study show that BFO has undergone eight types of changes that can be systematically explained by the extended evaluation schema. Finally, we discuss problematic cases, possible pitfalls and certain limits of our study that we propose to address in future work. <s> BIB005 </s> Evolution of biomedical ontologies and mappings: Overview of recent approaches <s> Change detection <s> Abstract Background Mappings established between life science ontologies require significant efforts to maintain them up to date due to the size and frequent evolution of these ontologies. In consequence, automatic methods for applying modifications on mappings are highly demanded. The accuracy of such methods relies on the available description about the evolution of ontologies, especially regarding concepts involved in mappings. However, from one ontology version to another, a further understanding of ontology changes relevant for supporting mapping adaptation is typically lacking. Methods This research work defines a set of change patterns at the level of concept attributes, and proposes original methods to automatically recognize instances of these patterns based on the similarity between attributes denoting the evolving concepts. This investigation evaluates the benefits of the proposed methods and the influence of the recognized change patterns to select the strategies for mapping adaptation. Results The summary of the findings is as follows: (1) the Precision (>60%) and Recall (>35%) achieved by comparing manually identified change patterns with the automatic ones; (2) a set of potential impact of recognized change patterns on the way mappings is adapted. We found that the detected correlations cover ∼66% of the mapping adaptation actions with a positive impact; and (3) the influence of the similarity coefficient calculated between concept attributes on the performance of the recognition algorithms. Conclusions The experimental evaluations conducted with real life science ontologies showed the effectiveness of our approach to accurately characterize ontology evolution at the level of concept attributes. This investigation confirmed the relevance of the proposed change patterns to support decisions on mapping adaptation. <s> BIB006
First of all, biomedical ontologies are much bigger than those of other domains so it is hard to see changes between ontology versions at a glance. Moreover, since there is no standard language for documenting changes occurring in ontologies, Diff computation approaches are especially important to identify changes between different ontology versions. PromptDiff was the first relevant initiative able to identify the differences between two ontology versions BIB001 . More recently, COnto-Diff BIB004 offered the user a way to specify change patterns and dedicated rules to determine a more compact and semantically more expressive diff representation. The compact diff representation covers complex ontology changes such as merging, splitting and moving of concepts or the addition and deletion of large subgraphs. Fig. 5 shows some important change types that are detected by COnto-Diff. Recently, Yingjie et al.  introduced a method to detect conflicts between several sequences of ontology changes. For instance, such conflicts can occur during collaborative ontology evolution where different stakeholders and viewpoints are usually involved. The study combines change detection and inconsistency checking methods in order to identify conflicting change sequences in ontology evolution. Moreover, Dos Reis et al. defined lexical and semantic change patterns based on the evolution of several medical ontologies BIB006 . These change patterns allow to characterize the way attribute values of concepts evolve, e.g., if the observed changes are likely to modify the meaning of an attribute value. The change operations and change patterns as determined by diff algorithms and other methods are very useful to maintain ontology-based mappings and other dependent applications. The need for a retrospective identification of differences between versions can be avoided during editing when ontology changes are well-documented including reasons of changes. For instance, it is important to document change operations in an upper level formal ontology such as Basic Formal Ontology (BFO) in order to allow for an appropriate change propagation into dependent domain ontologies BIB005 . Evolutionary terminology auditing (ETA) allows for measuring the quality improvements of formal ontologies and different kinds of terminologies over successive versions, and requires that ontology editors keep track of changes and their motivation for the respective changes (e.g., BIB002 BIB003 ). However, so far there is no standard language for documenting ontology evolution.