Evolution of biomedical ontologies and mappings: Overview of recent approaches <s> Adaptation of ontology-based mappings <s> Ontologies are heavily developed and used in life sciences and undergo continuous changes. However, the evolution of life science ontologies and references to them (e.g., annotations) is not well understood and has received little attention so far. We therefore propose a generic framework for analyzing both the evolution of ontologies and the evolution of ontology-related mappings, in particular annotations referring to ontologies and similarity (match) mappings between ontologies. We use our framework for an extensive comparative evaluation of evolution measures for 16 life science ontologies. Moreover, we analyze the evolution of annotation mappings and ontology mappings for the Gene Ontology. <s> BIB001 </s> Evolution of biomedical ontologies and mappings: Overview of recent approaches <s> Adaptation of ontology-based mappings <s> Existing biomedical ontologies encode scientific knowledge that is exploited in ontology-based annotated entities, e.g., genes described using Gene Ontology (GO) annotations. Ontology-based annotations correspond to building blocks for computing relatedness between annotated entities, as well as for data mining techniques that attempt to discover domain patterns or suggest novel associations among annotated entities. However, effectiveness of these annotation-based approaches can be considerably impacted by the quality of the annotations, and models that allow for the description of the quality of the annotations are required to validate and explain the behavior of these approaches. We propose AnnEvol, a framework to describe datasets of ontology-based annotated entities in terms of evolutionary properties of the annotations of these entities over time. AnnEvol complements state-of-the-art approaches that perform an annotation-wise description of the datasets, and conducts an annotation set-wise description which characterizes the evolution of annotations into semantically similar annotations. We empirically evaluate the expressiveness power of AnnEvol in a set of proteins annotated with GO using UniProt-GOA and Swiss-Prot. Our experimental results suggest that AnnEvol captures evolutionary behavior of the studied GO annotations, and clearly differentiates patterns of annotations depending on both the annotation provider and on the model organism of the studied proteins. <s> BIB002 </s> Evolution of biomedical ontologies and mappings: Overview of recent approaches <s> Adaptation of ontology-based mappings <s> We define and illustrate the mapping maintenance problem and its specificities.We categorize, review and discuss the literature on mapping maintenance.We provide unaddressed research challenges to cope with mapping maintenance. In several domains, software applications have intensively used Knowledge Organization Systems (KOS) like database schemas, ontologies, taxonomies and thesauri and their associated semantic correspondences (i.e., mappings). This underlines the relevance and capabilities of KOS and mappings to manage and integrate vast amounts of data. However, the dynamic nature of domain knowledge forces knowledge engineers to constantly modify KOS, to keep them up to date and useful. In this context, the maintenance of mappings affected by KOS evolution still remains an open research issue. Although this problem appears relevant for many different computer science fields, ranging from database to artificial intelligence, literature has so far only superficially addressed it to enable more flexible, automatic and precise solutions. This article presents, discusses and compares existing approaches for maintaining mappings and describes open research challenges. <s> BIB003
One of the additional challenges of ontology evolution is to keep dependent artifacts such as ontology-based mappings up-to-date. Several evolution studies in the life science domain (e.g., BIB001  BIB002 ) showed frequent and continuous changes for both, the considered ontologies and ontology-based mappings. In particular, the results in  showed significant instabilities for mappings created by automatic ontology matching techniques, e.g., utilizing the similarity of concept names and their synonyms for deriving correspondences. These observations underline the importance of (semi-) automatic adaptation strategies that can reuse and extend previous mappings instead of completely recomputing the mappings when an ontology changes. In the following we will first discuss requirements for the adaptation of ontology-based mappings. In Section 4.2 we will then discuss adaptation strategies for ontology-based mappings in the context of ontology evolution and compare them based on the introduced requirements. Approaches for the more general problem of mapping maintenance and repair are discussed in BIB003 .