Git4Voc: Git-based Versioning for Collaborative Vocabulary Development

Collaborative vocabulary development in the context of data integration is the process of finding consensus between the experts of the different systems and domains. The complexity of this process is increased with the number of involved people, the variety of the systems to be integrated and the dynamics of their domain. In this paper we advocate that the realization of a powerful version control system is the heart of the problem. Driven by this idea and the success of Git in the context of software development, we investigate the applicability of Git for collaborative vocabulary development. Even though vocabulary development and software development have much more similarities than differences there are still important differences. These need to be considered within the development of a successful versioning and collaboration system for vocabulary development. Therefore, this paper starts by presenting the challenges we were faced with during the creation of vocabularies collaboratively and discusses its distinction to software development. Based on these insights we propose Git4Voc which comprises guidelines how Git can be adopted to vocabulary development. Finally, we demonstrate how Git hooks can be implemented to go beyond the plain functionality of Git by realizing vocabulary-specific features like syntactic validation and semantic diffs

Étude de l'évolution du modèle de l'utilisateur des systèmes de construction collaborative d'ontologies

National audienceCet article rend compte d'une étude en cours sur l'évolution du modèle de l'utilisateur de systèmes de construction collaborative d'ontologies. Par modèle de l'utilisateur (ou modèle du contributeur), nous entendons la représentation que les concepteurs se font des utilisateurs de leurs systèmes et plus généralement des contributeurs à la construction des ontologies. Nous décrivons : 1) la méthode que nous utilisons pour étudier l'évolution du modèle de l'utilisateur ; 2) l'évolution de ce modèle (en termes de types d'utilisateurs, de caractérisations de l'utilisateur et de caractérisations de l'environnement de l'utilisateur) ; 3) les évolutions parallèles : a) des méthodes de conception des systèmes collaboratifs ; b) des systèmes eux-mêmes ; et c) des méthodes de construction collaborative des ontologies. Nous mentionnons quelques perspectives d'évolution envisagées par les concepteurs eux-mêmes. Cette étude vise à faire ressortir l'importance d'acquérir une meilleure connaissance des contributeurs potentiels à la construction collaborative des ontologies afin d'obtenir des outils collaboratifs mieux adaptés à ces contributeurs

Designing novel abstraction networks for ontology summarization and quality assurance

Biomedical ontologies are complex knowledge representation systems. Biomedical ontologies support interdisciplinary research, interoperability of medical systems, and Electronic Healthcare Record (EHR) encoding. Ontologies represent knowledge using concepts (entities) linked by relationships. Ontologies may contain hundreds of thousands of concepts and millions of relationships. For users, the size and complexity of ontologies make it difficult to comprehend “the big picture” of an ontology\u27s content. For ontology editors, size and complexity make it difficult to uncover errors and inconsistencies. Errors in an ontology will ultimately affect applications that utilize the ontology. In prior studies abstraction networks (AbNs) were developed to provide a compact summary of an ontology\u27s content and structure. AbNs have been shown to successfully support ontology summarization and quality assurance (QA), e.g., for SNOMED CT and NCIt. Despite the success of these previous studies, several major, unaddressed issues affect the applicability and usability of AbNs. This thesis is broken into five major parts, each addressing one issue. The first part of this dissertation addresses the scalability of AbN-based QA techniques to large SNOMED CT hierarchies. Previous studies focused on relatively small hierarchies. The QA techniques developed for these small hierarchies do not scale to large hierarchies, e.g., Procedure and Clinical finding. A new type of AbN, called a subtaxonomy, is introduced to address this problem. Subtaxonomies summarize a subset of an ontology\u27s content. Several types of subtaxonomies and subtaxonomy-based QA studies are discussed. The second part of this dissertation addresses the need for summarization and QA methods for the twelve SNOMED CT hierarchies with no lateral relationships. Previously developed SNOMED CT AbN derivation methodologies, which require lateral relationships, cannot be applied to these hierarchies. The Tribal Abstraction Network (TAN) is a new type of AbN derived using only hierarchical relationships. A TAN-based QA methodology is introduced and the results of a QA review of the Observable entity hierarchy are reported. The third part focuses on the development of generic AbN derivation methods that are applicable to groups of structurally similar ontologies, e.g., those developed in the Web Ontology Language (OWL) format. Previously, AbN derivation techniques were applicable to only a single ontology at a time. AbNs that are applicable to many OWL ontologies are introduced, a preliminary study on OWL AbN granularity is reported on, and the results of several QA studies are presented. The fourth part describes Diff Abstraction Networks, which summarize and visualize the structural differences between two ontology releases. Diff Area Taxonomy and Diff Partial-area Taxonomy derivation methodologies are introduced and Diff Partial-area taxonomies are derived for three OWL ontologies. The Diff Abstraction Network approach is compared to the traditional ontology diff approach. Lastly, tools for deriving and visualizing AbNs are described. The Biomedical Layout Utility Framework is introduced to support the automatic creation, visualization, and exploration of abstraction networks for SNOMED CT and OWL ontologies

Visualising Changes in a System of Linked Lightweight Ontologies

Ontologioiden avulla pyritään mallintamaan tiettyä alaa määrittelemällä kyseiseen alaan kuuluvia käsitteitä ja näiden välisiä yhteyksiä. Näitä käsitteitä voidaan käyttää alaa käsittelevän aineiston annotointiin. Lisäksi ontologioita voidaan linkittää toisiin ontologioihin, jolloin päästään hyödyntämään muualla tehtyä työtä. Ontologioita joudutaan luonnollisesti päivittämään, kun uusia käsitteitä tai muuta muutoksia tulee alalle. Tällöin myös kyseiseen ontologiaan linkittyneitä ontologioita voi joutua muuttamaan, jotta linkittyneet ontologiat pysyvät keskenään konsistentteina. Tässä työssä luotiin MUTU-työkalu auttamaan ontologiakehittäjää edellä kuvatussa muutostilanteessa. Työkalu välittää muutokset ontologiakehittäjälle, lajittelee muutokset muutoksen tyypin mukaan sekä esittää nämä muutokset linkitetyn ontologian kehittäjälle. Tieteellisenä tuloksena työkalu sisältää algoritmin, joka pyrkii ennustamaan, milloin toisessa ontologiassa tapahtunut muutos aiheuttaa muutoksen kehittäjän ontologiaan. Työkalun tavoitteena on helpottaa linkitetyn ontologian päivitystä varsinkin suurilla muutosmäärillä. Työkalun luomia muutoslistoja sekä muutosten lajittelun toimivuutta testattiin ontologiakehittäjillä todellisessa käyttötilanteessa. Työn tuloksena oli, että lajitteluheuristiikka toimi aineistossa hyvin käsitteiden ominaisuuksien muutoksissa ja auttaisi täten priorisoimaan läpikäytäviä muutoksia ontologiaa päivitettäessä.Ontologies aim to model a field by listing concepts and their relationships in the field. These concepts are then used in annotating data regarding the field. In addition, ontologies can be linked to other ontologies, thus utilising the work and knowledge of other ontology developers. Naturally, ontologies need to evolve due to the changes and advances of the field. In this case, also other ontologies linked to the ontology might require updates to ensure the consistency of the ontologies. In this work, I created a tool for assisting the ontology developer in the above described update. The tool was named mutu and it conveys the changes to the ontology developer, classifies the changes by their type and visualises the changes for the developer. As a scientific contribution, this work introduces an algorithm for classifying changes by their priority. The algorithm aims to predict which changes in the related ontology will cause changes in the ontology of the developer. The aim of the tool is to aid in updates with a large amount of changes. The created change lists and the performance of the change classification were tested in a real-life update task. The outcome of the evaluation was that the priority classification performance was good with the changes of property values, thus being an aid in prioritising changes in updating ontologies

Reasoning-Supported Quality Assurance for Knowledge Bases

The increasing application of ontology reuse and automated knowledge acquisition tools in ontology engineering brings about a shift of development efforts from knowledge modeling towards quality assurance. Despite the high practical importance, there has been a substantial lack of support for ensuring semantic accuracy and conciseness. In this thesis, we make a significant step forward in ontology engineering by developing a support for two such essential quality assurance activities

Change impact analysis for evolving ontology-based content management

Ontologies have become ubiquitous tools to embed semantics into content and applications on the semantic web. They are used to define concepts in a domain and allow us to reach at a common understanding on subjects of interest. Ontologies cover wide range of topics enabling both humans and machines to understand meanings and to reason in different contexts. They cover topics such as semantic web, artificial intelligence, information retrieval, machine translation, software development, content management, etc. We use ontologies for semantic annotation of content to facilitate understandability of the content by humans and machines. However, building ontology and annotations is often a manual process which is error prone and time consuming. Ontologies and ontology-driven content management systems (OCMS) evolve due to a change in conceptualization, the representation or the specification of the domain knowledge. These changes are often immense and frequent. Implementing the changes and adapting the OCMS accordingly require a huge effort. This is due to complex impacts of the changes on the ontologies, the content and dependent applications. Thus, evolving the OCMS with minimum and predictable impacts is among the top priorities of evolution in OCMS. We approach the problem of evolution by proposing a framework which clearly represents the interactions of the components of an OCMS. We proposed a layered OCMS framework which contains an ontology layer, content layer and annotation layer. Further, we propose a novel approach for analysing impacts of change operations. Impacts of atomic change operations are assigned individually by analysing the target entity and all the other entities that are structurally or semantically dependent on it. Impacts of composite change operations are analysed following three stage process. We use impact cancellation, impact balancing and impact transformation to analyse the impacts when two or more atomic changes are executed as part of a composite or domain specific change operation. We build a model which estimates the impacts of a complete change operation enabling the ontology engineer to specify the weight associated with each optimization criteria. Finally, the model identifies the implementation strategy with minimum cost of evolution. We evaluate our system by building a prototype as a proof of concept and find out encouraging results

Actes des 29es Journées Francophones d'Ingénierie des Connaissances, IC 2018

International audienc

Building Ontologies Collaboratively Using ContentCVS

OWL Ontologies are already being used in many application domains. In particular, OWL is extensively used in the clinical sciences; prominent examples of OWL ontologies are the National Cancer Institute (NCI) Thesaurus, SNOMED CT, the Gene Ontology (GO), the Foundational Model of Anatomy (FMA), and GALEN. These ontologies are large and complex; for example, SNOMED currently describes more than 350.000 concepts whereas NCI and GALEN describe around 50.000 concepts. Furthermore, these ontologies are in continuous evolution; for example the developers of NCI and GO perform approximately 350 additions of new entities and 25 deletions of obsolete entities each month [1]. Most realistic ontologies, including the ones just mentioned, are being developed collaboratively. The developers of an ontology can be geographically distributed and may contribute in different ways and to different extents. Maintaining such large ontologies in a collaborative way is a highly complex process, which involves tracking and managing the frequent changes to the ontology, reconciling conflicting views of the domain from different developers, minimising the introduction of errors (e.g., ensurin