Evolution of biomedical ontologies and mappings: Overview of recent approaches <s> Approaches <s> In this paper, we introduce a general overview of Falcon-AO: a practical ontology matching system with acceptable to good performance and a number of remarkable features. Furthermore, Falcon-AO is one of the best systems in all kinds of tests in the latest three years' OAEI campaigns. Falcon-AO is written in Java, and is open source. <s> BIB001 </s> Evolution of biomedical ontologies and mappings: Overview of recent approaches <s> Approaches <s> TaxoMap is an alignment tool which aim is to discover rich correspondences between concepts. It performs an oriented alignment (from a source to a target ontology) and takes into account labels and sub-class descriptions. Our participation in last year edition of the competition have put the emphasis on certain limits. TaxoMap 2 is a new implementation of TaxoMap that reduces significantly runtime and enables parameterization by specifying the ontology language and different thresholds used to extract different mapping relations. The new implementation stresses on terminological techniques, it takes into account synonymy, and multi-label description of concepts. Special effort was made to handle large-scale ontologies by partitioning input ontologies into modules to align. We conclude the paper by pointing out the necessary improvements that need to be made. <s> BIB002 </s> Evolution of biomedical ontologies and mappings: Overview of recent approaches <s> Approaches <s> Systems or software agents do not always agree on the information being shared, justifying the use of distinct ontologies for the same domain. For achieving interoperability, declarative mappings are used as a basis for exchanging information between systems. However, in dynamic environments like the Web and the Semantic Web, ontologies constantly evolve, potentially leading to invalid ontology mappings. This paper presents two approaches for managing ontology mapping evolution: a user-centric approach in which the user defines the mapping evolution strategies to be applied automatically by the system, and a semanticbased approach, in which the ontology’s evolution logs are exploited to capture the semantics of changes and then adapted to (and applied on at) the ontology mapping evolution process. <s> BIB003 </s> Evolution of biomedical ontologies and mappings: Overview of recent approaches <s> Approaches <s> BackgroundNumerous ontologies have recently been developed in life sciences to support a consistent annotation of biological objects, such as genes or proteins. These ontologies underlie continuous changes which can impact existing annotations. Therefore, it is valuable for users of ontologies to study the stability of ontologies and to see how many and what kind of ontology changes occurred.ResultsWe present OnEX (Ontology Evolution EXplorer) a system for exploring ontology changes. Currently, OnEX provides access to about 560 versions of 16 well-known life science ontologies. The system is based on a three-tier architecture including an ontology version repository, a middleware component and the OnEX web application. Interactive workflows allow a systematic and explorative change analysis of ontologies and their concepts as well as the semi-automatic migration of out-dated annotations to the current version of an ontology.ConclusionOnEX provides a user-friendly web interface to explore information about changes in current life science ontologies. It is available at http://www.izbi.de/onex. <s> BIB004 </s> Evolution of biomedical ontologies and mappings: Overview of recent approaches <s> Approaches <s> The continuous evolution of life science ontologies requires the adaptation of their associated mappings. We propose two approaches for tackling this problem in a largely automatic way: (1) a composition-based adaptation relying on the principle of mapping composition and (2) a diff-based adaptation algorithm individually handling change operations to update the mapping. Both techniques reuse unaffected correspondences, and adapt only the affected mapping part. We experimentally assess and confirm the effectiveness of our approaches for evolving mappings between large life science ontologies. <s> BIB005 </s> Evolution of biomedical ontologies and mappings: Overview of recent approaches <s> Approaches <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> BIB006 </s> Evolution of biomedical ontologies and mappings: Overview of recent approaches <s> Approaches <s> The highly dynamic nature of domain ontologies has a direct impact on semantic mappings established between concepts from different ontologies. Mappings must therefore be maintained according to ongoing ontology changes. Since many software applications exploit mappings for managing information and knowledge, it is important to define appropriate adaptation strategies to apply to existing mappings in order to keep their validity over time. In this article, we propose a set of mapping adaptation actions and present how they are used to maintain mappings up-to-date based on ontology change operations of different nature. We conduct an experimental evaluation using life sciences ontologies and mappings. We measure the evolution of mappings based on the proposed approach to mapping adaptation. The results confirm that mappings must be individually adapted according to the different types of ontology change. <s> BIB007 </s> Evolution of biomedical ontologies and mappings: Overview of recent approaches <s> Approaches <s> Knowledge Organization Systems (KOSs) are extensively used in the biomedical domain to support information sharing between software applications. KOSs are proposed covering different, but overlapping subjects, and mappings indicate the semantic relation between concepts from two KOSs. Over time, KOSs change as do the mappings between them. This can result from a new discovery or a revision of existing knowledge which includes corrections of concepts or mappings. Indeed, changes affecting KOS entities may force the underline mappings to be updated in order to ensure their reliability over time. To tackle this open research problem, we study how mappings are affected by KOS evolution. This article presents a detailed descriptive analysis of the impact that changes in KOS have on mappings. As a case study, we use the official mappings established between SNOMED CT and ICD-9-CM from 2009 to 2011. Results highlight factors according to which KOS changes in varying degrees influence the evolution of mappings. <s> BIB008 </s> Evolution of biomedical ontologies and mappings: Overview of recent approaches <s> Approaches <s> Information on the web and web services that are revised by stakeholders is growing incredibly. The presentation of this information has shifted from a representational model of web information with loosely clustered terminology to semi-formal terminology and even to formal ontology. Mediation (i.e., mapping) is required for systems and services to share information. Mappings are established between ontologies in order to resolve terminological and conceptual incompatibilities. Due to new discoveries in the field of information sharing, the body of knowledge has become more structured and refined. The domain ontologies that represent bodies of knowledge need to be able to accommodate new information. This allows for the ontology to evolve from one consistent state to another. Changes in resources cause existing mappings between ontologies to be unreliable and stale. This highlights the need for mapping evolution (regeneration) as it would eliminate the discrepancies from the existing mappings. In order to re-establish the mappings between dynamic ontologies, the existing systems require a complete mapping process to be restructured, and this process is time consuming. This paper proposes a mapping reconciliation approach between the updated ontologies that has been found to take less time to process compared to the time of existing systems when only the changed resources are considered and also eliminates the staleness of the existing mappings. The proposed approach employs the change history of ontology in order to store the ontology change information, which helps to drastically reduce the reconciliation time of the mappings between dynamic ontologies. A comprehensive evaluation of the performance of the proposed system on standard data sets has been conducted. The experimental results of the proposed system in comparison with six existing mapping systems are provided in this paper using 13 different data sets, which support our claims. <s> BIB009
We will now discuss existing adaptation approaches for ontologybased mappings that are affected by ontology evolution. Four approaches have been explicitly proposed to adapt ontology mappings. One further approach is highly related since it deals with the adaptation of ontology-based annotations as a consequence of ontology evolution. The different approaches are summarized in Table 1 w.r.t. the posed requirements. In the following, we first introduce the main idea for each approach and then comparatively discuss the approaches. The first approach to automatically evolve or adapt ontology mappings has been proposed by Martins and Silva BIB003 . Their aim is to resolve possible mapping inconsistencies depending on the previously applied ontology evolution strategy. The authors distinguish between elementary changes in ontology mappings such as additions and deletions of attribute values in source or target concepts, as well as composite changes like updates of attribute values. The mapping evolution process tries to identify the previously applied ontology evolution process for every affected correspondence. The authors discuss a userdriven and a semantic mapping evolution process. In particular, they discuss one mapping evolution strategy in case of concept deletions in detail, but do not focus on other change types. It remains unclear if all possibly invalid correspondences will be adapted by their approach. The evaluation uses small exemplary ontologies of 15-25 concepts and does not consider the quality of the adapted mappings. Hartung et al. BIB004 developed the web tool OnEX that also supports the adaptation of biomedical annotations. The system first computes basic change operations between the old and new ontology version. According to the type of ontology change the system proposes one or more possibilities to adapt an affected annotation. OnEX provides basic mapping adaptation strategies for information-reducing change operations such as concept deletion, setting concepts to obsolete or concept fusion but not for information-extending operations like concept additions. The approach can be applied for several predetermined life science ontologies but has not been specified formally and was not evaluated. Khattak et al.  BIB009 present an automatic adaptation approach relying on a partial re-computation of ontology mappings that are affected by ontology evolution. The approach uses a Change History Log (CHL)  to detect ontology changes such as create, update, delete for concepts and attributes. Changed elements in the source or target ontology of a mapping are automatically matched with the complete current version of the other ontology. The approach only reuses the completely unaffected part of a mapping, discards all affected correspondences (independent of the change type) and adds all newly computed correspondences (output of the matching step). In the evaluation, mappings between different life science ontologies such as Adult Mouse Anatomy Ontology (MA) and NCIT are automatically generated by different match tools (e.g., Falcon BIB001 , TaxoMap BIB002 ). Then 25 ontology changes (mainly additions) are induced manually, i.e., it does not rely on real ontology versions. The studies show an improvement w.r.t. execution times compared to the complete mapping re-computation but does not evaluate the quality of the produced mappings. Groß et al. BIB005 5 present two approaches for adapting ontology mappings. The composition-based and diff-based adaptation approaches both rely on the reuse of existing mappings (e.g. M O1,O2 in Fig. 3 ) as well as the use of evolution mappings (e.g. between O1 and O1' in Fig. 3 ). The first approach uses a composition of the old ontology mapping with an evolution mapping containing the semantic correspondences between the old and new ontology version. Mapping composition makes use of the transitivity criterion where two correspondences (a, b, =) (a ∈ O1 , b ∈ O2) and (b, c, =) (b ∈ O2 , c ∈ O2′) are combined to a new correspondence (a, c, =). The authors propose a set of rules to achieve the correct semantic type for the migrated correspondence, e.g. two equality correspondences can be combined to one equality correspondence. Complex cases like the combination of one less general (a, b, b) with one more general correspondence (b, c,N) cannot be resolved automatically. In these cases the user can be involved to decide for the correct type. The second approach makes use of a diff evolution mapping covering individual ontology changes computed by COnto-Diff BIB006 as well as a set of change handlers to migrate affected correspondences according to the change type. The approach applies the same semantic type rules as the composition-based approach. The diff-based approach can handle basic changes like attribute value changes as well as complex change types such as concept splits or merges. The evaluation analyzes the quality of adapted mappings between three very large life science ontologies (NCIT, SNOMED CT, Foundational Model of Anatomy) and could show a very high effectiveness, in particular for the diff-based approach, with F-Measure values between 90% and 94%. Dos Reis et al. BIB007 5 propose a similar approach than the diff-based scheme of BIB005 using so-called mapping adaptation actions (MAAs) to keep mappings up-to-date for different ontology changes. Ontology changes are computed using the COnto-Diff algorithm BIB006 and further categorized into revision, deletion and addition of ontology elements (C, A, R). Moreover, the authors distinguish between different mapping changes (remove, addition, move, derivation, modification) and propose one MAA for each mapping change type. For instance, the derivation is a composed action where an existing correspondence is reused as a modified copy of this existing correspondence. The modification action supports the adaptation of mappings with different types of semantic relations instead of only considering equivalence correspondences. The evaluation analyzes ontology and mapping changes for three large life science ontologies and existing mapping versions between them (NCIT, SNOMED CT, ICD-9-CM). The evaluation does not assess the quality by computing F-Measure values for migrated mappings, but instead identifies the effectiveness of the approach by computing proportions of the proposed MAAs as an actual consequence of different ontology change types. These results vary depending on the types of ontology and mapping changes, e.g. 65% of the toObsolete operations lead to a correspondence adaptation by replacing the obsolete source concept with its super concept. Table 1 summarizes the discussed approaches for different criteria addressing the introduced requirements. Most approaches produce valid mappings w.r.t. the new ontology version(s). To detect changes between ontology versions, two of the approaches BIB005 BIB007 use a complex diff evolution mapping covering semantically meaningful change operations such as split or merge. Three approaches rely on a basic diff evolution mapping that covers the basic change operations add, delete and update for concepts, relationships and attributes. The two adaptation approaches BIB005 BIB007 are most advanced as they also consider not only complex changes but also new ontology concepts to find additional correspondences and they support semantic mappings with both equality and more/less general relations. The importance of supporting nonequality relationships in ontology mappings is confirmed in a further study BIB008 for mappings between SNOMED CT and ICD, since SNOMED CT tends to cover additional and more detailed knowledge. Overall three approaches BIB005 BIB009 BIB007 generate new correspondences by applying standard ontology matching techniques to align added concepts from one of the ontologies with the respective other ontology.