Evolution of biomedical ontologies and mappings: Overview of recent approaches
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paper_title: BioPortal: A Web Repository for Biomedical Ontologies and Data Resources

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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.
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paper_title: Chemical Entities of Biological Interest: an update

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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.
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paper_title: Bio-ontologies: current trends and future directions

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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.
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paper_title: Management of Dynamic Biomedical Terminologies: Current Status and Future Challenges

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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.
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paper_title: The role of frame-based representation on the semantic web

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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.
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paper_title: Ontology evolution: a process-centric survey

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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.
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paper_title: SNOMED-CT: The advanced terminology and coding system for eHealth.

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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.
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paper_title: Ontology Matching: State of the Art and Future Challenges

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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.
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paper_title: A large-scale evaluation of computational protein function prediction

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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.
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paper_title: A survey on annotation tools for the biomedical literature

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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.
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paper_title: Target-driven merging of taxonomies with Atom

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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.
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paper_title: NCI Thesaurus: A semantic model integrating cancer-related clinical and molecular information

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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.
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paper_title: The Unified Medical Language System (UMLS): Integrating Biomedical Terminology

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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.
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paper_title: A translation approach to portable ontology specifications

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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.
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paper_title: Gene Set Enrichment Analysis

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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.
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paper_title: FCA-MERGE: Bottom-Up Merging of Ontologies

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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.
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paper_title: Enriching ontology mappings with semantic relations

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There is a large number of tools to match or align corresponding concepts between ontologies. Most tools are restricted to equality correspondences, although many concepts may be related differently, e.g. according to an is-a or part-of relationship. Supporting such additional semantic correspondences can greatly improve the expressiveness of ontology mappings and their usefulness for tasks such as ontology merging and ontology evolution. We present a new approach called STROMA (SemanTic Refinement of Ontology MAppings) to determine semantic ontology mappings. In contrast to previous approaches, it follows a so-called enrichment strategy that refines the mappings determined with a state-of-the-art match tool. The enrichment strategy employs several techniques including the use of background knowledge and linguistic approaches to identify the additional kinds of correspondences. We evaluate the approach in detail using several real-life benchmark tests. A comparison with different tools for semantic ontology matching confirms the viability of the proposed enrichment strategy.
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paper_title: Management of Dynamic Biomedical Terminologies: Current Status and Future Challenges

paper_content:

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.
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paper_title: Ontology evolution: a process-centric survey

paper_content:

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.
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paper_title: Applying the Realism-Based Ontology-Versioning Method for Tracking Changes in the Basic Formal Ontology

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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.
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paper_title: PROMPTDIFF: A Fixed-Point Algorithm for Comparing Ontology Versions

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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.
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paper_title: Applying evolutionary terminology auditing to the Gene Ontology

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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.
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paper_title: COnto-Diff: generation of complex evolution mappings for life science ontologies

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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.
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paper_title: Recognizing lexical and semantic change patterns in evolving life science ontologies to inform mapping adaptation

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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.
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paper_title: Applying Evolutionary Terminology Auditing to SNOMED CT

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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.
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paper_title: Summarizing and visualizing structural changes during the evolution of biomedical ontologies using a Diff Abstraction Network

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Display Omitted Diff Abstraction Networks are introduced to summarize and visualize ontology change.The Diff Area Taxonomy (DAT) and the Diff Partial-area Taxonomy (DPAT) are introduced.DPATs are derived for three ontologies and are shown to highlight important changes.DPATs offer a compact way of viewing many changes due to various editing operations. Biomedical ontologies are a critical component in biomedical research and practice. As an ontology evolves, its structure and content change in response to additions, deletions and updates. When editing a biomedical ontology, small local updates may affect large portions of the ontology, leading to unintended and potentially erroneous changes. Such unwanted side effects often go unnoticed since biomedical ontologies are large and complex knowledge structures. Abstraction networks, which provide compact summaries of an ontology's content and structure, have been used to uncover structural irregularities, inconsistencies and errors in ontologies. In this paper, we introduce Diff Abstraction Networks ("Diff AbNs"), compact networks that summarize and visualize global structural changes due to ontology editing operations that result in a new ontology release. A Diff AbN can be used to support curators in identifying unintended and unwanted ontology changes. The derivation of two Diff AbNs, the Diff Area Taxonomy and the Diff Partial-area Taxonomy, is explained and Diff Partial-area Taxonomies are derived and analyzed for the Ontology of Clinical Research, Sleep Domain Ontology, and eagle-i Research Resource Ontology. Diff Taxonomy usage for identifying unintended erroneous consequences of quality assurance and ontology merging are demonstrated.
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paper_title: CODEX: exploration of semantic changes between ontology versions

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Summary: Life science ontologies substantially change over time to meet the requirements of their users and to include the newest domain knowledge. Thus, an important task is to know what has been modified between two versions of an ontology (diff ). This diff should contain all performed changes as compact and understandable as possible. We present CODEX (Complex Ontology Diff Explorer), a tool that allows determining semantic changes between two versions of an ontology which users can interactively analyze in multiple ways. Availability and Implementation: CODEX is available under http: //www.izbi.de/codex and is supported by all major browsers. It is implemented in Java based on Google Web Toolkit technology. Additionally, users can access a web service interface to use the diff functionality in their applications and analyses.
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paper_title: WebProtégé: A Collaborative Web Based Platform for Editing Biomedical Ontologies

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Summary: WebProtege is an open-source Web application for editing OWL 2 ontologies. It contains several features to aid collaboration, including support for the discussion of issues, change notification and revision-based change tracking. WebProtege also features a simple user interface, which is geared towards editing the kinds of class descriptions and annotations that are prevalent throughout biomedical ontologies. Moreover, it is possible to configure the user interface using views that are optimized for editing Open Biomedical Ontology (OBO) class descriptions and metadata. Some of these views are shown in the Supplementary Material and can be seen in WebProtege itself by configuring the project as an OBO project. ::: ::: Availability and implementation: WebProtege is freely available for use on the Web at http://webprotege.stanford.edu. It is implemented in Java and JavaScript using the OWL API and the Google Web Toolkit. All major browsers are supported. For users who do not wish to host their ontologies on the Stanford servers, WebProtege is available as a Web app that can be run locally using a Servlet container such as Tomcat. Binaries, source code and documentation are available under an open-source license at http://protegewiki.stanford.edu/wiki/WebProtege. ::: ::: Contact: ude.drofnats@egdirroh.wehttam ::: ::: Supplementary information: Supplementary data are available at Bioinformatics online.
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paper_title: Visualizing the Evolution of Ontologies: A Dynamic Graph Perspective

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Ontologies can be represented as graphs, since they essentially comprise a set of interconnected concepts describing a certain field of knowledge. Consequently, ontologies are often visualized as graphs, using different visual notations and common graph drawing techniques. The visualization of static graphs has been researched a lot, but when it comes to time-varying graphs, researchers face much more challenges in order to design useful, readable, and intuitive visualizations. If we have to deal with dynamic, i.e., evolving and time-dependent ontologies, we have to adapt existing visualization techniques to this challenging problem or develop new ones. In this position paper, we take a look at the visual representation of time-varying ontologies, and provide a discussion from a dynamic graph visualization perspective.
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paper_title: OnEX: Exploring changes in life science ontologies

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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.
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paper_title: Analysis and Prediction of User Editing Patterns in Ontology Development Projects

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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.
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paper_title: Predicting the Extension of Biomedical Ontologies

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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.
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paper_title: State-of-the-art on mapping maintenance and challenges towards a fully automatic approach

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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.
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paper_title: AnnEvol: An Evolutionary Framework to Description Ontology-Based Annotations

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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.
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paper_title: Analyzing the evolution of life science ontologies and mappings

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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.
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paper_title: A USER-DRIVEN AND A SEMANTIC-BASED ONTOLOGY MAPPING EVOLUTION APPROACH

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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.
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paper_title: Mapping evolution of dynamic web ontologies

paper_content:

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.
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paper_title: Understanding semantic mapping evolution by observing changes in biomedical ontologies

paper_content:

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.
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paper_title: COnto-Diff: generation of complex evolution mappings for life science ontologies

paper_content:

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.
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paper_title: Semi-Automatic Adaptation of Mappings between Life Science Ontologies

paper_content:

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.
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paper_title: Mapping adaptation actions for the automatic reconciliation of dynamic ontologies

paper_content:

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.
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paper_title: TaxoMap in the OAEI 2008 Alignment Contest

paper_content:

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.
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paper_title: Falcon-AO: A practical ontology matching system

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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.
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paper_title: OnEX: Exploring changes in life science ontologies

paper_content:

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.
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paper_title: Semi-Automatic Adaptation of Mappings between Life Science Ontologies

paper_content:

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.
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paper_title: Mapping adaptation actions for the automatic reconciliation of dynamic ontologies

paper_content:

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.
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paper_title: Enriching ontology mappings with semantic relations

paper_content:

There is a large number of tools to match or align corresponding concepts between ontologies. Most tools are restricted to equality correspondences, although many concepts may be related differently, e.g. according to an is-a or part-of relationship. Supporting such additional semantic correspondences can greatly improve the expressiveness of ontology mappings and their usefulness for tasks such as ontology merging and ontology evolution. We present a new approach called STROMA (SemanTic Refinement of Ontology MAppings) to determine semantic ontology mappings. In contrast to previous approaches, it follows a so-called enrichment strategy that refines the mappings determined with a state-of-the-art match tool. The enrichment strategy employs several techniques including the use of background knowledge and linguistic approaches to identify the additional kinds of correspondences. We evaluate the approach in detail using several real-life benchmark tests. A comparison with different tools for semantic ontology matching confirms the viability of the proposed enrichment strategy.
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paper_title: Applying the Realism-Based Ontology-Versioning Method for Tracking Changes in the Basic Formal Ontology

paper_content:

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.
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paper_title: Uberon, an integrative multi-species anatomy ontology

paper_content:

We present Uberon, an integrated cross-species ontology consisting of over 6,500 classes representing a variety of anatomical entities, organized according to traditional anatomical classification criteria. The ontology represents structures in a species-neutral way and includes extensive associations to existing species-centric anatomical ontologies, allowing integration of model organism and human data. Uberon provides a necessary bridge between anatomical structures in different taxa for cross-species inference. It uses novel methods for representing taxonomic variation, and has proved to be essential for translational phenotype analyses. Uberon is available at http://uberon.org
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paper_title: Target-driven merging of taxonomies with Atom

paper_content:

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.
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paper_title: Chemical Entities of Biological Interest: an update

paper_content:

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.
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paper_title: Roadmap for a Multilingual BioPortal

paper_content:

Ontology indexes and repositories are important in the realization of the Semantic Web; however, the need has clearly moved to multilingual capabilities that are hard to offer when dealing with multiple ontologies, originally in different formats and contributed by an open community. In this paper, we present a roadmap for addressing the issues of dealing with multilingual or monolingual ontologies in BioPortal, the reference ontology repository in biomedicine, currently mostly English-oriented. We propose a set of representations to support multilingualism in the portal and to enable a complete use of the functionalities and services for any kind of ontologies and data. While encouraging the community to use the best available specifications to represent multilingual content e.g., Lemon; our objective is to handle multilingualism in a proper semantically rich and consistent manner in the ontology repository. We are currently deploying and implementing these representations in a local instance of BioPortal for French ontologies.
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paper_title: Health multi-terminology portal: a semantic added-value for patient safety.

paper_content:

Since the mid-90s, several quality-controlled health gateways were developed. In France, CISMeF is the leading health gateway. It indexes Internet resources from the main institutions, using the MeSH thesaurus and the Dublin Core metadata element set. Since 2005, the CISMeF Information System (IS) includes 24 health terminologies, classifications and thesauri for indexing and information retrieval. This work aims at creating a Health Multi-Terminology Portal (HMTP) and connect it to the CISMeF Terminology Database mainly for searching concepts and terms among all the health controlled vocabularies available in French (or in English and translated in French) and browsing it dynamically. To integrate the terminologies in the CISMeF IS, three steps are necessary: (1) designing a meta-model into which each terminology can be integrated, (2) developing a process to include terminologies into the HMTP, (3) building and integrating existing and new inter-terminology mappings into the HMTP. A total of 24 terminologies are included in the HMTP, with 575,300 concepts, 852,000 synonyms, 222,800 definitions and 1,180,000 relations. Heightteen of these terminologies are not included yet in the UMLS among them, some from the World Health Organization. Since January 2010, HMTP is daily used by CISMeF librarians to index in multi-terminology mode. A health multiterminology portal is a valuable tool helping the indexing and the retrieval of resources from a quality-controlled patient safety gateway. It can also be very useful for teaching or performing audits in terminology management.
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Title: Evolution of biomedical ontologies and mappings: Overview of recent approaches

Section 1: Introduction
Description 1: Introduce the importance of biomedical ontologies, their applications, and the need for ontology evolution and mappings.

Section 2: Problem formulation
Description 2: Introduce the basic scenario of ontology and mapping evolution with illustrative examples and definitions.

Section 3: Ontology evolution
Description 3: Discuss recent investigations and ongoing efforts in the field of ontology evolution, including change detection, visualization, and prediction.

Section 4: Change detection
Description 4: Detail the methods and tools developed for detecting changes between different versions of ontologies.

Section 5: Visualizing ontology evolution
Description 5: Explore the importance and methods of visualizing ontology evolution to aid user understanding and application.

Section 6: Ontology change prediction
Description 6: Explain approaches for predicting ontology changes and their importance for managing the evolution process.

Section 7: Adaptation of ontology-based mappings
Description 7: Discuss the challenges and strategies for adapting ontology-based mappings to manage the evolution of ontologies.

Section 8: Requirements
Description 8: Identify the essential requirements for (semi-) automatic mapping adaptation strategies to be useful for applications and users.

Section 9: Approaches
Description 9: Introduce and compare existing adaptation approaches for ontology-based mappings in the context of ontology evolution.

Section 10: Discussion
Description 10: Discuss the evaluation results, user interactions, and remaining challenges in the evolution of ontology-based mappings.

Section 11: Open challenges and future directions
Description 11: Outline the unmet needs and potential future research directions in the evolution of semantic, formal, merged, and multilingual ontologies and mappings.

Section 12: Conclusion
Description 12: Summarize the main points discussed in the survey and their implications for future research and applications.