Evolution of biomedical ontologies and mappings: Overview of recent approaches <s> Introduction <s> Abstract To support the sharing and reuse of formally represented knowledge among AI systems, it is useful to define the common vocabulary in which shared knowledge is represented. A specification of a representational vocabulary for a shared domain of discourse—definitions of classes, relations, functions, and other objects—is called an ontology. This paper describes a mechanism for defining ontologies that are portable over representation systems. Definitions written in a standard format for predicate calculus are translated by a system called Ontolingua into specialized representations, including frame-based systems as well as relational languages. This allows researchers to share and reuse ontologies, while retaining the computational benefits of specialized implementations. We discuss how the translation approach to portability addresses several technical problems. One problem is how to accommodate the stylistic and organizational differences among representations while preserving declarative content. Another is how to translate from a very expressive language into restricted languages, remaining system-independent while preserving the computational efficiency of implemented systems. We describe how these problems are addressed by basing Ontolingua itself on an ontology of domain-independent, representational idioms. <s> BIB001 </s> Evolution of biomedical ontologies and mappings: Overview of recent approaches <s> Introduction <s> We believe that to build the Semantic Web, the sharing of ontological information is required. This allows agents to reach partial shared understanding and thus interoperate. We are proposing frame-based representation as a suitable paradigm for building ontologies as well as the World Wide Web Consortium's RDF-formalism (and its extensions, such as the DARPA Agent Markup Language) as a manifestation of frame-based representation for the Web. <s> BIB002 </s> Evolution of biomedical ontologies and mappings: Overview of recent approaches <s> Introduction <s> Ontologies have been established for knowledge sharing and are widely used as a means for conceptually structuring domains of interest. With the growing usage of ontologies, the problem of overlapping knowledge in a common domain becomes critical. We propose the new method FCA-MERGE for merging ontologies following a bottom-up approach which offers a structural description of the merging process. The method is guided by application-specific instances of the given source ontologies, that are to be merged. We apply techniques from natural language processing and formal concept analysis to derive a lattice of concepts as a structural result of FCA-MERGE. The generated result is then explored and transformed into the merged ontology with human interaction. <s> BIB003 </s> Evolution of biomedical ontologies and mappings: Overview of recent approaches <s> Introduction <s> The Unified Medical Language System (http://umlsks.nlm.nih.gov) is a repository of biomedical vocabularies developed by the US National Library of Medicine. The UMLS integrates over 2 million names for some 900 000 concepts from more than 60 families of biomedical vocabularies, as well as 12 million relations among these concepts. Vocabularies integrated in the UMLS Metathesaurus include the NCBI taxonomy, Gene Ontology, the Medical Subject Headings (MeSH), OMIM and the Digital Anatomist Symbolic Knowledge Base. UMLS concepts are not only inter-related, but may also be linked to external resources such as GenBank. In addition to data, the UMLS includes tools for customizing the Metathesaurus (MetamorphoSys), for generating lexical variants of concept names (lvg) and for extracting UMLS concepts from text (MetaMap). The UMLS knowledge sources are updated quarterly. All vocabularies are available at no fee for research purposes within an institution, but UMLS users are required to sign a license agreement. The UMLS knowledge sources are distributed on CD-ROM and by FTP. <s> BIB004 </s> Evolution of biomedical ontologies and mappings: Overview of recent approaches <s> Introduction <s> In recent years, as a knowledge-based discipline, bioinformatics has been made more computationally amenable. After its beginnings as a technology advocated by computer scientists to overcome problems of heterogeneity, ontology has been taken up by biologists themselves as a means to consistently annotate features from genotype to phenotype. In medical informatics, artifacts called ontologies have been used for a longer period of time to produce controlled lexicons for coding schemes. In this article, we review the current position in ontologies and how they have become institutionalized within biomedicine. As the field has matured, the much older philosophical aspects of ontology have come into play. With this and the institutionalization of ontology has come greater formality. We review this trend and what benefits it might bring to ontologies and their use within biomedicine. <s> BIB005 </s> Evolution of biomedical ontologies and mappings: Overview of recent approaches <s> Introduction <s> A clinical terminology is essential for Electronic Health records. It represents clinical information input into clinical IT systems by clinicians in a machine-readable manner. Use of a Clinical Terminology, implemented within a clinical information system, will enable the delivery of many patient health benefits including electronic clinical decision support, disease screening and enhanced patient safety. For example, it will help reduce medication-prescribing errors, which are currently known to kill or injure many citizens. It will also reduce clinical administration effort and the overall costs of healthcare. <s> BIB006 </s> Evolution of biomedical ontologies and mappings: Overview of recent approaches <s> Introduction <s> Over the last 8 years, the National Cancer Institute (NCI) has launched a major effort to integrate molecular and clinical cancer-related information within a unified biomedical informatics framework, with controlled terminology as its foundational layer. The NCI Thesaurus is the reference terminology underpinning these efforts. It is designed to meet the growing need for accurate, comprehensive, and shared terminology, covering topics including: cancers, findings, drugs, therapies, anatomy, genes, pathways, cellular and subcellular processes, proteins, and experimental organisms. The NCI Thesaurus provides a partial model of how these things relate to each other, responding to actual user needs and implemented in a deductive logic framework that can help maintain the integrity and extend the informational power of what is provided. This paper presents the semantic model for cancer diseases and its uses in integrating clinical and molecular knowledge, more briefly examines the models and uses for drug, biochemical pathway, and mouse terminology, and discusses limits of the current approach and directions for future work. <s> BIB007 </s> Evolution of biomedical ontologies and mappings: Overview of recent approaches <s> Introduction <s> Biomedical ontologies provide essential domain knowledge to drive data integration, information retrieval, data annotation, natural-language processing, and decision support. The National Center for Biomedical Ontology is developing BioPortal, a Web-based system that serves as a repository for biomedical ontologies. Bio-Portal defines relationships among those ontologies and between the ontologies and online data resources such as PubMed, ClinicalTrials.gov, and the Gene Expression Omnibus (GEO). Bio-Portal supports not only the technical requirements for access to biomedical ontologies either via Web browsers or via Web services, but also community-based participation in the evaluation and evolution of ontology content. BioPortal enables ontology users to learn what biomedical ontologies exist, what a particular ontology might be good for, and how individual ontologies relate to one another. BioPortal is available online at http://alpha.bioontology.org. <s> BIB008 </s> Evolution of biomedical ontologies and mappings: Overview of recent approaches <s> Introduction <s> Set enrichment analytical methods have become commonplace tools applied to the analysis and interpretation of biological data. The statistical techniques are used to identify categorical biases within lists of genes, proteins, or metabolites. The goal is to discover the shared functions or properties of the biological items represented within the lists. Application of these methods can provide great biological insight, including the discovery of participation in the same biological activity or pathway, shared interacting genes or regulators, common cellular compartmentalization, or association with disease. The methods require ordered or unordered lists of biological items as input, understanding of the reference set from which the lists were selected, categorical classifiers describing the items, and a statistical algorithm to assess bias of each classifier. Due to the complexity of most algorithms and the number of calculations performed, computer software is almost always used for execution of the algorithm, as well as for presentation of the results. This chapter will provide an overview of the statistical methods used to perform an enrichment analysis. Guidelines for assembly of the requisite information will be presented, with a focus on careful definition of the sets used by the statistical algorithms. The need for multiple test correction when working with large libraries of classifiers is emphasized, and we outline several options for performing the corrections. Finally, interpreting the results of such analysis will be discussed along with examples of recent research utilizing the techniques. <s> BIB009 </s> Evolution of biomedical ontologies and mappings: Overview of recent approaches <s> Introduction <s> Chemical Entities of Biological Interest (ChEBI) is a freely available dictionary of molecular entities focused on ‘small’ chemical compounds. The molecular entities in question are either natural products or synthetic products used to intervene in the processes of living organisms. Genome-encoded macromolecules (nucleic acids, proteins and peptides derived from proteins by cleavage) are not as a rule included in ChEBI. In addition to molecular entities, ChEBI contains groups (parts of molecular entities) and classes of entities. ChEBI includes an ontological classification, whereby the relationships between molecular entities or classes of entities and their parents and/or children are specified. ChEBI is available online at http://www.ebi.ac.uk/chebi/. This article reports on new features in ChEBI since the last NAR report in 2007, including substructure and similarity searching, a submission tool for authoring of ChEBI datasets by the community and a 30-fold increase in the number of chemical structures stored in ChEBI. <s> BIB010 </s> Evolution of biomedical ontologies and mappings: Overview of recent approaches <s> Introduction <s> After years of research on ontology matching, it is reasonable to consider several questions: is the field of ontology matching still making progress? Is this progress significant enough to pursue further research? If so, what are the particularly promising directions? To answer these questions, we review the state of the art of ontology matching and analyze the results of recent ontology matching evaluations. These results show a measurable improvement in the field, the speed of which is albeit slowing down. We conjecture that significant improvements can be obtained only by addressing important challenges for ontology matching. We present such challenges with insights on how to approach them, thereby aiming to direct research into the most promising tracks and to facilitate the progress of the field. <s> BIB011 </s> Evolution of biomedical ontologies and mappings: Overview of recent approaches <s> Introduction <s> Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. Here we report the results from the first large-scale community-based critical assessment of protein function annotation (CAFA) experiment. Fifty-four methods representing the state of the art for protein function prediction were evaluated on a target set of 866 proteins from 11 organisms. Two findings stand out: (i) today's best protein function prediction algorithms substantially outperform widely used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is considerable need for improvement of currently available tools. <s> BIB012 </s> Evolution of biomedical ontologies and mappings: Overview of recent approaches <s> Introduction <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> BIB013 </s> Evolution of biomedical ontologies and mappings: Overview of recent approaches <s> Introduction <s> New approaches to biomedical text mining crucially depend on the existence of comprehensive annotated corpora. Such corpora, commonly called gold standards, are important for learning patterns or models during the training phase, for evaluating and comparing the performance of algorithms and also for better understanding the information sought for by means of examples. Gold standards depend on human understanding and manual annotation of natural language text. This process is very time-consuming and expensive because it requires high intellectual effort from domain experts. Accordingly, the lack of gold standards is considered as one of the main bottlenecks for developing novel text mining methods. This situation led the development of tools that support humans in annotating texts. Such tools should be intuitive to use, should support a range of different input formats, should include visualization of annotated texts and should generate an easy-to-parse output format. Today, a range of tools which implement some of these functionalities are available. In this survey, we present a comprehensive survey of tools for supporting annotation of biomedical texts. Altogether, we considered almost 30 tools, 13 of which were selected for an in-depth comparison. The comparison was performed using predefined criteria and was accompanied by hands-on experiences whenever possible. Our survey shows that current tools can support many of the tasks in biomedical text annotation in a satisfying manner, but also that no tool can be considered as a true comprehensive solution. <s> BIB014 </s> Evolution of biomedical ontologies and mappings: Overview of recent approaches <s> Introduction <s> Ontology evolution aims at maintaining an ontology up to date with respect to changes in the domain that it models or novel requirements of information systems that it enables. The recent industrial adoption of Semantic Web techniques, which rely on ontologies, has led to the increased importance of the ontology evolution research. Typical approaches to ontology evolution are designed as multiple-stage processes combining techniques from a variety of fields (e.g., natural language processing and reasoning). However, the few existing surveys on this topic lack an in-depth analysis of the various stages of the ontology evolution process. This survey extends the literature by adopting a process-centric view of ontology evolution. Accordingly, we first provide an overall process model synthesized from an overview of the existing models in the literature. Then we survey the major approaches to each of the steps in this process and conclude on future challenges for techniques aiming to solve that particular stage. <s> BIB015 </s> Evolution of biomedical ontologies and mappings: Overview of recent approaches <s> Introduction <s> Objectives: Controlled terminologies and their dependent artefacts provide a consensual understanding of a domain while reducing ambiguities and enabling reasoning. However, the evolution of a domain's knowledge directly impacts these terminologies and generates inconsistencies in the underlying biomedical information systems. In this article, we review existing work addressing the dynamic aspect of terminologies as well as their effects on mappings and semantic annotations. Methods: We investigate approaches related to the identification, characterization and propagation of changes in terminologies, mappings and semantic annotations including techniques to update their content. Results and conclusion: Based on the explored issues and existing methods, we outline open research challenges requiring investigation in the near future. <s> BIB016
Ontologies have gained much importance in the past two decades, especially in the biomedical domain BIB005  . Many different ontologies have been developed in various sub-disciplines. For instance, BioPortal BIB008 currently provides access to more than 500 different biomedical ontologies. Ontologies consist of defined concepts, that are typically structured within trees or acyclic graphs where the concept nodes are interconnected by is-a, part-of and other semantic relationships. One main application of ontologies is the semantic annotation of different kinds of data objects. For instance, the well-known Gene Ontology (GO) is used to describe molecular functions of genes and proteins  and to predict new gene functions BIB009 . Chemical entities can be described by the Chemical Entities of Biological Interest (ChEBI) ontology BIB010 , and concepts of medical ontologies like SNOMED CT BIB006 are assigned to documents like electronic health records (EHRs) or case report forms (CRFs). However it is important to note, that in the biomedical domain the term "ontology" is often not used in the sense of formal, axiombased ontologies but instead for a wide spectrum of simpler terminologies including a.o. thesauri, taxonomies and is-a-hierarchies. The wellknown definition of Gruber "An ontology is an explicit specification of a conceptualization." BIB001 leaves room for variation w.r.t. to the detail of specification BIB002 . There is a wide spectrum of ontologies of varying expressiveness ranging from simple controlled vocabularies and thesauri to informal and formal "is-a" structures, and, at the highest level of expressiveness, formal ontologies that specify disjoint classes, part-whole relationships and further kinds of logical constraints BIB002 . The W3C provides a definition for different kinds of non-formal ontologies and calls them knowledge organization systems (KOS). KOS denote a.o. thesauri, classification schemes, subject heading systems and taxonomies and can be expressed by the Simple Knowledge Organization System (SKOS) data model  . Throughout the paper we will use the term "ontology" for ontologies of varying expressiveness as done by most of the relevant work on biomedical ontology and ontology evolution. Often there are several ontologies within one domain and they can contain overlapping information. Mappings between such related ontologies interrelate or link corresponding and semantically related concepts and are of high importance for data integration and ontologybased query and analysis tasks. For instance, these mappings support merging several related ontologies into one ontology (e.g. BIB013 BIB003 ). A prominent huge integrated data source is the Unified Medical Language System (UMLS) BIB004 built out of more than 100 biomedical ontologies. Moreover, ontology mappings can support a semantic search since ontology-based queries can be enhanced by involving additional ontologies that are interconnected via mappings. Typically, an ontology mapping covers a set of semantic correspondences (links) between the concepts of two different ontologies. The semi-automatic determination of ontology mappings (ontology matching) has been an active research area for more than a decade  BIB011 . Similarly numerous approaches have been proposed to determine biomedical annotation BIB014 BIB012 , i.e., to link biomedical objects or documents to describing ontology concepts. Such methods produce recommendations that support domain experts in finding correct and complete ontology mappings and annotations. Usually, ontologies are not static but modified on a regular basis. This process is known as ontology evolution. For instance, ontologies need to be changed to incorporate new domain knowledge, remove design errors or to achieve changed requirements. Often ontology development is a collaborative process that is supported by tools such as Protégé  or OBO-Edit  . In the life sciences, many ontology consortia continuously release new ontology versions. For instance, GO releases a new version every day, while the National Cancer Institute Thesaurus (NCIT) BIB007 is published on a monthly basis. Fig. 1 exemplarily shows the history of changes between 2015-11 and 2016-04 in GO. Typically, new versions contain improved and extended knowledge such as new concepts (classes), relationships or attributes like synonyms. However, existing knowledge can also be revised or removed, e.g. concepts might be deleted or marked as obsolete. For instance in the shown time period for GO (see Fig. 1 ), new classes have been added continuously, in 03-2016 some concepts were set to obsolete, and some definitions and class labels have been deleted. To manage the evolution of ontologies it is essential to determine changes, e.g. by analyzing change logs or by computing the difference (Diff) between two given versions of an ontology. Such a Diff is useful to synchronize changes in collaborative ontology development and to adapt dependent applications. The evolution of ontologies has impact on ontology-based applications. For instance, ontology mappings and annotations can become invalid when the underlying ontologies are changed. This is especially critical in highly volatile domains such as the life sciences. Fig. 2 illustrates two ontologies (O1 and O2) and a mapping between them (M O1,O2 ). In O1, one concept has been removed (red) while two concepts have been added to O2 (green). Another concept in O2 has been revised (blue) e.g., by changing the concept name. These ontology changes have impact on the set of correspondences (dashed lines) and might require changes in the mapping. In the example, one correspondence is associated to a deleted concept, and might therefore be removed. Moreover, the added and revised concept might lead to novel correspondences. Hence, ontology-based mappings can become out-dated as a consequence of ontology evolution. In order to keep mappings up-to-date they need to be migrated to currently valid ontology versions. On the one hand, a manual mapping maintenance can be very time consuming or even infeasible since ontologies and mappings can become very large. On the other hand, automated methods could be simply reapplied on the same data to obtain a valid mapping w.r.t. the current ontology version. However, this can lead to a huge loss in quality since existing mappings might have been manually verified and corrected in the meantime. Just recomputing the results would discard this valuable knowledge. Moreover, usually a smaller part of an ontology is changed such that it seems likely to adapt only affected mapping parts. Therefore, it is useful to apply (semi-) automatic adaptation methods to migrate out-dated ontology-based mapping to currently valid ontology versions. In this review, we will first introduce the problem of ontology and mapping evolution (Section 2) and then give an overview of recently proposed evolution methods for the biomedical domain and discuss open challenges: • Methods for ontology evolution have been surveyed in several contexts before (e.g.   BIB015 BIB016 ). Here we will focus on recent approaches that we see relevant for semi-automatic adaptation of ontologybased mappings and applications in the life sciences. This includes novel directions in ontology change detection and prediction and the visualization of ontology evolution. (Section 3) • We will then discuss requirements for mapping evolution and provide a comparison and overview on existing (semi-) automatic adaptation strategies for ontology-based mappings. (Section 4) • We will finally outline open challenges and future directions for the evolution of ontologies and ontology-based mappings and applications (Section 5).