Evolution of biomedical ontologies and mappings: Overview of recent approaches <s> Ontology change prediction <s> Developing and extending a biomedical ontology is a very demanding task that can never be considered complete given our ever-evolving understanding of the life sciences. Extension in particular can benefit from the automation of some of its steps, thus releasing experts to focus on harder tasks. Here we present a strategy to support the automation of change capturing within ontology extension where the need for new concepts or relations is identified. Our strategy is based on predicting areas of an ontology that will undergo extension in a future version by applying supervised learning over features of previous ontology versions. We used the Gene Ontology as our test bed and obtained encouraging results with average f-measure reaching 0.79 for a subset of biological process terms. Our strategy was also able to outperform state of the art change capturing methods. In addition we have identified several issues concerning prediction of ontology evolution, and have delineated a general framework for ontology extension prediction. Our strategy can be applied to any biomedical ontology with versioning, to help focus either manual or semi-automated extension methods on areas of the ontology that need extension. <s> BIB001 </s> Evolution of biomedical ontologies and mappings: Overview of recent approaches <s> Ontology change prediction <s> The development of real-world ontologies is a complex undertaking, commonly involving a group of domain experts with different expertise that work together in a collaborative setting. These ontologies are usually large scale and have complex structures. To assist in the authoring process, ontology tools are key at making the editing process as streamlined as possible. Being able to predict confidently what the users are likely to do next as they edit an ontology will enable us to focus and structure the user interface accordingly and to facilitate more efficient interaction and information discovery. In this paper, we use data mining, specifically the association rule mining, to investigate whether we are able to predict the next editing operation that a user will make based on the change history. We simulated and evaluated continuous prediction across time using sliding window model. We used the association rule mining to generate patterns from the ontology change logs in the training window and tested these patterns on logs in the adjacent testing window. We also evaluated the impact of different training and testing window sizes on the prediction accuracies. At last, we evaluated our prediction accuracies across different user groups and different ontologies. Our results indicate that we can indeed predict the next editing operation a user is likely to make. We will use the discovered editing patterns to develop a recommendation module for our editing tools, and to design user interface components that better fit with the user editing behaviors. <s> BIB002
In the last years, the tracking and prediction of ontology evolution has gained attention. This is of special interest for collaborative ontology editing and development as well as for the migration of ontology-based applications. Also change prediction methods can not guarantee to be perfectly correct and precise, they can support users in planing and managing adaptation processes, e.g. by precociously indicating possibly impacted parts of dependent mappings and applications. Current relevant work includes  where the authors focused on tracking the collaborative processes behind the evolution of an ontology, i.e., the changes made by contributors over time. Wang et al. investigate the way ontology editors behave when they modify an ontology and predict future modifications BIB002 . Moreover, Pesquita & Couto used machine learning techniques to predict which branch of the Gene Ontology is likely to expand in the future release using supervised learning methods BIB001 . Tsatsaronis et al. implement temporal classifiers to predict future extension of the MeSH controlled terminology using MeSH-indexed PubMed articles  .