565 research outputs found

    INTEROP deliverable DTG 6.2 : Method repository

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    This deliverable presents the INTEROP method chunks repository (MCR), its architecture and provided services. It includes the definition of a reusable method chunk, its structure, illustrated with examples of method chunks stored in the repository and guidelines for method chunks definition and characterisation covering tasks TG6.2 and TG6.3 of the work plan of the task group. The main result is the definition of the structure of the method chunk repository emphasizing the link to interoperability. Interoperability is a first-class concept in the structure of the method chunk repository. It not only characterizes method chunks, i.e. procedures to solve interoperability problems, but also interoperability cases, i.e. the presentation of actual problems involving interoperability issues. TG 6 has produced three MCR prototypes. Two experiments were undertaken using the Metis system and one using ConceptBase. The task group attended a two-day intense workshop on Metis. As a result, two experiments with Metis as platform for the method chunk repository are under way and reported in this deliverable. One is realizing the structure of the MCR as specified in this report. The other is an alternative approach that serves as a benchmark and is reported in the appendix. The ConceptBase prototype utilizes the metamodel presented in this deliverable. We have analysed three cases involving various aspects of interoperability. One case is about establishing a broker platform for insurance agents, the second about linking the information systems in the public utility sector, and the third case is establishing the relation of the ATHENA Model-Driven Interoperability Framework to the goals of the MCR. The results of the TG6 have been published at the ISD conference 2006 and the ER conference 2006. Copies of the papers are included in the appendix. The report of the example session with the method chunk repository has been shifted towards deliverable TG6.3 (Tutorial of the MCR). This is the more logical place. We want to emphasize that TG6 was not only busy in drafting concepts, exploring the state of the art, and analyzing cases. We are actually experimenting with a prototype and consider this a valuable contribution to the network. As soon as the prototype is stable, knowledge about interoperability solutions can be coded in this repository and can guide designers of interoperable systems by experience knowledge

    From Method Fragments to Method Services

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    In Method Engineering (ME) science, the key issue is the consideration of information system development methods as fragments. Numerous ME approaches have produced several definitions of method parts. Different in nature, these fragments have nevertheless some common disadvantages: lack of implementation tools, insufficient standardization effort, and so on. On the whole, the observed drawbacks are related to the shortage of usage orientation. We have proceeded to an in-depth analysis of existing method fragments within a comparison framework in order to identify their drawbacks. We suggest overcoming them by an improvement of the ?method service? concept. In this paper, the method service is defined through the service paradigm applied to a specific method fragment ? chunk. A discussion on the possibility to develop a unique representation of method fragment completes our contribution

    Quality assurance for digital learning object repositories: issues for the metadata creation process

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    Metadata enables users to find the resources they require, therefore it is an important component of any digital learning object repository. Much work has already been done within the learning technology community to assure metadata quality, focused on the development of metadata standards, specifications and vocabularies and their implementation within repositories. The metadata creation process has thus far been largely overlooked. There has been an assumption that metadata creation will be straightforward and that where machines cannot generate metadata effectively, authors of learning materials will be the most appropriate metadata creators. However, repositories are reporting difficulties in obtaining good quality metadata from their contributors, and it is becoming apparent that the issue of metadata creation warrants attention. This paper surveys the growing body of evidence, including three UK-based case studies, scopes the issues surrounding human-generated metadata creation and identifies questions for further investigation. Collaborative creation of metadata by resource authors and metadata specialists, and the design of tools and processes, are emerging as key areas for deeper research. Research is also needed into how end users will search learning object repositories

    Component-based situational methods

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    International audienceThe work presented in this paper is related to the area of Situational Method Engineering (SME) which focuses on project-specific method construction. We propose a faceted framework to understand and classify issues in system development SME. The framework identifies four different but complementary viewpoints. Each view allows us to capture a particular aspect of situational methods. Inter-relationships between these views show how they influence each other. In order to study, understand and classify a particular view of SME in its diversity, we associate a set of facets with each view. As a facet allows an in-depth description of one specific aspect of SME, the views show the variety and diversity of these aspects

    Interoperability for individual learner centred accessibility for web-based educational systems

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    This paper describes the interoperability underpinning a new strategy for delivering accessible computer-based resources to individual learners based on their specified needs and preferences in the circumstances in which they are operating. The new accessibility strategy, known as AccessForAll, augments the model of universal accessibility of resources by engaging automated systems and builds upon the previous development of libraries of suitable resources and components. It focuses on individual learners and their particular accessibility needs and preferences. It fits within an inclusive framework for educational accommodation that supports accessibility, mobility, cultural, language and location appropriateness and increases educational flexibility. Its effectiveness will depend upon widespread use that will exploit the 'network effect' to increase the content available for accessibility and distribute the responsibility for the availability of accessible resources across the globe. Widespread use will depend upon the interoperability of AccessForAll implementations that, in turn, will depend on the success of the four major aspects of their interoperability: structure, syntax, semantics and systemic adoption

    Site-Specific Rules Extraction in Precision Agriculture

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    El incremento sostenible en la producción alimentaria para satisfacer las necesidades de una población mundial en aumento es un verdadero reto cuando tenemos en cuenta el impacto constante de plagas y enfermedades en los cultivos. Debido a las importantes pérdidas económicas que se producen, el uso de tratamientos químicos es demasiado alto; causando contaminación del medio ambiente y resistencia a distintos tratamientos. En este contexto, la comunidad agrícola divisa la aplicación de tratamientos más específicos para cada lugar, así como la validación automática con la conformidad legal. Sin embargo, la especificación de estos tratamientos se encuentra en regulaciones expresadas en lenguaje natural. Por este motivo, traducir regulaciones a una representación procesable por máquinas está tomando cada vez más importancia en la agricultura de precisión.Actualmente, los requisitos para traducir las regulaciones en reglas formales están lejos de ser cumplidos; y con el rápido desarrollo de la ciencia agrícola, la verificación manual de la conformidad legal se torna inabordable.En esta tesis, el objetivo es construir y evaluar un sistema de extracción de reglas para destilar de manera efectiva la información relevante de las regulaciones y transformar las reglas de lenguaje natural a un formato estructurado que pueda ser procesado por máquinas. Para ello, hemos separado la extracción de reglas en dos pasos. El primero es construir una ontología del dominio; un modelo para describir los desórdenes que producen las enfermedades en los cultivos y sus tratamientos. El segundo paso es extraer información para poblar la ontología. Puesto que usamos técnicas de aprendizaje automático, implementamos la metodología MATTER para realizar el proceso de anotación de regulaciones. Una vez creado el corpus, construimos un clasificador de categorías de reglas que discierne entre obligaciones y prohibiciones; y un sistema para la extracción de restricciones en reglas, que reconoce información relevante para retener el isomorfismo con la regulación original. Para estos componentes, empleamos, entre otra técnicas de aprendizaje profundo, redes neuronales convolucionales y “Long Short- Term Memory”. Además, utilizamos como baselines algoritmos más tradicionales como “support-vector machines” y “random forests”.Como resultado, presentamos la ontología PCT-O, que ha sido alineada con otras ontologías como NCBI, PubChem, ChEBI y Wikipedia. El modelo puede ser utilizado para la identificación de desórdenes, el análisis de conflictos entre tratamientos y la comparación entre legislaciones de distintos países. Con respecto a los sistemas de extracción, evaluamos empíricamente el comportamiento con distintas métricas, pero la métrica F1 es utilizada para seleccionar los mejores sistemas. En el caso del clasificador de categorías de reglas, el mejor sistema obtiene un macro F1 de 92,77% y un F1 binario de 85,71%. Este sistema usa una red “bidirectional long short-term memory” con “word embeddings” como entrada. En relación al extractor de restricciones de reglas, el mejor sistema obtiene un micro F1 de 88,3%. Este extractor utiliza como entrada una combinación de “character embeddings” junto a “word embeddings” y una red neuronal “bidirectional long short-term memory”.<br /

    Scalable Approaches for Auditing the Completeness of Biomedical Ontologies

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    An ontology provides a formalized representation of knowledge within a domain. In biomedicine, ontologies have been widely used in modern biomedical applications to enable semantic interoperability and facilitate data exchange. Given the important roles that biomedical ontologies play, quality issues such as incompleteness, if not addressed, can affect the quality of downstream ontology-driven applications. However, biomedical ontologies often have large sizes and complex structures. Thus, it is infeasible to uncover potential quality issues through manual effort. In this dissertation, we introduce automated and scalable approaches for auditing the completeness of biomedical ontologies. We mainly focus on two incompleteness issues -- missing hierarchical relations and missing concepts. To identify missing hierarchical relations, we develop three approaches: a lexical-based approach, a hybrid approach utilizing both lexical features and logical definitions, and an approach based on concept name transformation. To identify missing concepts, a lexical-based Formal Concept Analysis (FCA) method is proposed for concept enrichment. We also predict proper concept names for the missing concepts using deep learning techniques. Manual review by domain experts is performed to evaluate these approaches. In addition, we leverage extrinsic knowledge (i.e., external ontologies) to help validate the detected incompleteness issues. The auditing approaches have been applied to a variety of biomedical ontologies, including the SNOMED CT, National Cancer Institute (NCI) Thesaurus and Gene Ontology. In the first lexical-based approach to identify missing hierarchical relations, each concept is modeled with an enriched set of lexical features, leveraging words and noun phrases in the name of the concept itself and the concept\u27s ancestors. Given a pair of concepts that are not linked by a hierarchical relation, if the enriched lexical attributes of one concept is a superset of the other\u27s, a potentially missing hierarchical relation will be suggested. Applying this approach to the September 2017 release of SNOMED CT (US edition) suggested 38,615 potentially missing hierarchical relations. A domain expert reviewed a random sample of 100 potentially missing ones, and confirmed 90 are valid (a precision of 90%). In the second work, a hybrid approach is proposed to detect missing hierarchical relations in non-lattice subgraphs. For each concept, its lexical features are harmonized with role definitions to provide a more comprehensive semantic model. Then a two-step subsumption testing is performed to automatically suggest potentially missing hierarchical relations. This approach identified 55 potentially missing hierarchical relations in the 19.08d version of the NCI Thesaurus. 29 out of 55 were confirmed as valid by the curators from the NCI Enterprise Vocabulary Service (EVS) and have been incorporated in the newer versions of the NCI Thesaurus. 7 out of 55 further revealed incorrect existing hierarchical relations in the NCI Thesaurus. In the third work, we introduce a transformation-based method that leverages the Unified Medical Language System (UMLS) knowledge to identify missing hierarchical relations in its source ontologies. Given a concept name, noun chunks within it are identified and replaced by their more general counterparts to generate new concept names that are supposed to be more general than the original one. Applying this method to the UMLS (2019AB release), a total of 39,359 potentially missing hierarchical relations were detected in 13 source ontologies. Domain experts evaluated a random sample of 200 potentially missing hierarchical relations identified in the SNOMED CT (US edition), and 100 in the Gene Ontology. 173 out of 200 and 63 out of 100 potentially missing hierarchical relations were confirmed by domain experts, indicating our method achieved a precision of 86.5% and 63% for the SNOMED CT and Gene Ontology, respectively. In the work of concept enrichment, we introduce a lexical method based on FCA to identify potentially missing concepts. Lexical features (i.e., words appearing in the concept names) are considered as FCA attributes while generating formal context. Applying multistage intersection on FCA attributes results in newly formalized concepts along with bags of words that can be utilized to name the concepts. This method was applied to the Disease or Disorder sub-hierarchy in the 19.08d version of the NCI Thesaurus and identified 8,983 potentially missing concepts. We performed a preliminary evaluation and validated that 592 out of 8,983 potentially missing concepts were included in external ontologies in the UMLS. After obtaining new concepts and their relevant bags of words, we further developed deep learning-based approaches to automatically predict concept names that comply with the naming convention of a specific ontology. We explored simple neural network, Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN) combined with LSTM. Our experiments showed that the LSTM-based approach achieved the best performance with an F1 score of 63.41% for predicting names for newly added concepts in the March 2018 release of SNOMED CT (US Edition) and an F1 score of 73.95% for naming missing concepts revealed by our previous work. In the last part of this dissertation, extrinsic knowledge is leveraged to collect supporting evidence for the detected incompleteness issues. We present a work in which cross-ontology evaluation based on extrinsic knowledge from the UMLS is utilized to help validate potentially missing hierarchical relations, aiming at relieving the heavy workload of manual review
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