A layered framework for pattern-based ontology evolution

The challenge of ontology-driven modelling of information components is well known in both academia and industry. In this paper, we present a novel approach to deal with customisation and abstraction of ontology-based model evolution. As a result of an empirical study, we identify a layered change operator framework based on the granularity, domain-specificity and abstraction of changes. The implementation of the operator framework is supported through layered change logs. Layered change logs capture the objective of ontology changes at a higher level of granularity and support a comprehensive understanding of ontology evolution. The layered change logs are formalised using a graph-based approach. We identify the recurrent ontology change patterns from an ontology change log for their reuse. The identified patterns facilitate optimizing and improving the definition of domain-specific change patterns

Managing semantic Grid metadata in S-OGSA

Grid resources such as data, services, and equipment, are increasingly being annotated with descriptive metadata that facilitates their discovery and their use in the context of Virtual Organizations (VO). Making such growing body of metadata explicit and available to Grid services is key to the success of the VO paradigm. In this paper we present a model and management architecture for Semantic Bindings, i.e., firstclass Grid entities that encapsulate metadata on the Grid and make it available through predictable access patterns. The model is at the core of the S-OGSA reference architecture for the Semantic Grid

Requirements and services for metadata management

Knowledge-intensive applications pose new challenges to metadata management, including distribution, access control, uniformity of access, and evolution in time. The authors identify general requirements for metadata management and describe a simple model and service that focuses on RDF metadata to address these requirements

Layered change log model: bridging between ontology change representation and pattern mining

To date, no ontology change management system exists that records the ontology changes based on the different levels of granularity. Once changes are performed using elementary level change operations, they are recorded in the database at the elementary level accordingly. Such a change representation procedure is not sufficient to represent the intuition behind any applied change and thus, cannot capture the semantic impact of a change. In this paper, we discuss recording of the applied ontology changes in the form of a layered change log. We support the implementation of a layered change operator framework through layered change logs. We utilize the lower level ontology change log in two ways, i.e. recording of applied ontology changes (operational) and mining of higher level change patterns (analytical). The higher level change logs capture the objective of the ontology changes at a higher level of granularity and support a comprehensive understanding of the ontology evolution. The knowledge-based change log facilitates the detection of similarities within different time series, mining of change patterns and reuse of knowledge.The layered change logs are formalised using a graph-based approach

Ontology change management and identification of change patterns

Ontologies can support a variety of purposes, ranging from capturing the conceptual knowledge to the organisation of digital content and information. However, information systems are always subject to change and ontology change management can pose challenges. In this sense, the application and representation of ontology changes in terms of higher-level change operations can describe more meaningful semantics behind the applied change. In this paper, we propose a fourphase process that covers the operationalization, representation and detection of higherlevel changes in ontology evolution life cycle. We present different levels of change operators based on the granularity and domainspecificity of changes. The first layer is based on generic atomic level change operators, whereas the next two layers are user-defined (generic/domainspecific) change patterns. We introduce layered change logs for the explicit operational representation of ontology changes. We formalised the change log using a graph-based approach. We introduce a technique to identify composite changes that not only assists in formulating ontology change log data in a more concise manner, but also helps in realizing the semantics and intent behind any applied change. Furthermore, we identify frequent change sequences that are applied as a reference in order to discover reusable, often domainspecific and usagedriven change patterns. We describe the pattern identification algorithms and evaluate their performance

Ontology evolution: a process-centric survey

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

Folkoncept: método de suporte à modelagem conceitual de ontologias a partir da aquisição de conhecimentos de folksonomias

In this work, we present a method called Folkoncept for supporting conceptual modeling of ontologies starting with knowledge acquisition based on folksonomies. The method aims at helping actors enrolled in the development process in eliciting terms and in the modeling choice of how to represent these terms in the ontology. The objective of applying the Folkoncept method is to reduce the knowledge acquisition bottleneck through ontology learning techniques based on folksonomies. Folkoncept reaches three activities of the development process: knowledge acquisition, conceptual modeling, and evaluation, the latter being integrated into the conceptual modeling activity. With relation to the knowledge acquisition, Folkoncept deals with the retrieval, representation, and enrichment of terms (tags) coming from a folksonomy resulting from a social process of tagging performed by the actors involved in the ontology development process. In the conceptual modeling activity, Folkoncept helps the ontology designer to transform folksonomy’s tags into elements of the ontology being developed. In the ontology evaluation activity, the method helps ontology designers to validate the new elements that are suggested by the ontology learning method. In addition, the Folkoncept reduces the difficulty in using the OntoClean methodology making its use transparent to the ontology designer. Folkoncept was evaluated by means of ontology development experiments realized in a controlled environment by teams composed by ontology designers coming from the area of computing. Some teams worked with a prototype system that implements the Folkoncept. Results obtained by these teams were compared with the results from teams working without the system. The comparison was performed through metrics that show that the Folkoncept helped ontology designers to develop more descriptive ontologies with fewer errors with relation to the idealized taxonomies of OntoClean.Neste trabalho, apresenta-se um método para o desenvolvimento de ontologias a partir de folksonomias. O objetivo do método é auxiliar os atores envolvidos no processo de desenvolvimento na elicitação de termos a serem representados na ontologia e na tomada de decisão de como modelar tais termos. Busca-se, pela aplicação do método, reduzir o gargalo na aquisição de conhecimentos empregando-se técnicas de aprendizado de ontologias a partir de folksonomias. O método atinge três atividades do processo de desenvolvimento de ontologias: aquisição de conhecimentos, modelagem conceitual e avaliação das ontologias, sendo este último integrado à modelagem conceitual. Na aquisição de conhecimentos, o método trata da recuperação, representação e enriquecimento das etiquetas (termos) presentes nas folksonomias originadas de um processo social de etiquetagem realizado pelos atores envolvidos no desenvolvimento da ontologia. Na modelagem conceitual, auxilia o projetista a transformar as etiquetas das folksonomias em elementos da ontologia em desenvolvimento, ou seja, na modelagem de novos elementos. Na avaliação de ontologias, o método auxilia os projetistas na validação dos novos elementos que são sugeridos pelo método de aprendizado. Além disso, o método diminui a dificuldade em utilizar a metodologia OntoClean tornando sua aplicação transparente ao projetista. A avaliação do método foi realizada por meio de experimentos de desenvolvimento de ontologias em um ambiente controlado. Participaram dos experimentos equipes compostas por projetistas da área da computação, sendo que algumas equipes trabalharam com um protótipo que implementa o método e outras não. A avaliação foi realizada por meio de métricas que comprovaram que o sistema auxiliou os projetistas a desenvolverem ontologias mais descritivas e com número menor de erros de formalismo

Evolution cohérente des ressources termino-ontologiques et des annotations sémantiques

Un des enjeux du web sémantique est de produire des caractérisations formelles de contenus documentaires, ou annotations sémantiques, de qualité. Or dans un environnement dynamique, les ressources termino-ontologiques et les annotations sémantiques qu'elles permettent de construire doivent être modifiées régulièrement et en cohérence pour s'adapter à l'évolution du domaine concerné et des collections documentaires annotées. Notre contribution est une méthode qui permet de gérer conjointement l'évolution d'une ressource termino-ontologique et d'annotations sémantiques produites à partir de cette ressource. La méthode définit les types de changements applicables (élémentaires ou complexes), ainsi que des stratégies d'évolution de la ressource et des annotations. Cette méthode est mise en œuvre par le logiciel EvOnto qui s'intègre à l'environnement d'annotation automatique de documents TextViz défini dans le cadre du projet ANR DYNAMO. L'originalité d'EvOnto est de s'adapter à plusieurs scénarios d'évolution suivant que ce soit l'ontologie, la collection documentaire ou les annotations qui soient modifiées. EvOnto assure un support à l'ontologue en le guidant interactivement pour formuler une demande de changement, évaluer son impact (effets supplémentaires) sur la ressource termino-ontologique et aussi sur les annotations sémantiques, et décider ensuite de leur mise en œuvre. Il fournit des informations à l'ontologue pour qu'il décide d'une évolution en connaissant ses conséquences, et qu'il l'adapte pour minimiser les effets négatifs, les impacts non souhaitables ou les coûts correspondants sur la ressource elle-même et son utilisation dans des annotations.One of the challenges of the Semantic Web is to get high quality formal representations that characterize document content, also called semantic annotations. In a dynamic environment, the termino-ontological resources and semantic annotations built thanks to the resources must be regularly and consistently modified to adapt to the evolution of the domain to which they relate and to the annotated document collections. Our contribution is a method to jointly manage the evolution of a termino-ontological resource and semantic annotations built with this resource. The method defines applicable change types (elementary or complex) as well as evolution strategies for both the resource and the document semantic annotations. This method is supported by the EvOnto system that takes place in the TextViz platform for ontology-based automatic document annotation developed in the DYNAMO project. The originality of EvOnto is to preserve the consistency between the termino-ontological resources and the semantic annotations.. EvOnto provides support to the ontologist for different scenarios, and guides him interactively when he requests for a change by assessing its impact (additional effects) on the quality of the termino-ontological resource and also on semantic annotations, and then when he decides on their implementation. EvOnto provides the ontologist with relevant information on the use of ontology so that he takes initiative of a change knowing its consequences, and so that he adapts changes to minimize their negative effects, their undesirable impacts and their related costs on the resource itself and its use in annotations