Conceptual modeling for genomics: Building an integrated repository of open data

Many repositories of open data for genomics, collected by world-wide consortia, are important enablers of biological research; moreover, all experimental datasets leading to publications in genomics must be deposited to public repositories and made available to the research community. These datasets are typically used by biologists for validating or enriching their experiments; their content is documented by metadata. However, emphasis on data sharing is not matched by accuracy in data documentation; metadata are not standardized across the sources and often unstructured and incomplete. In this paper, we propose a conceptual model of genomic metadata, whose purpose is to query the underlying data sources for locating relevant experimental datasets. First, we analyze the most typical metadata attributes of genomic sources and define their semantic properties. Then, we use a top-down method for building a global-as-view integrated schema, by abstracting the most important conceptual properties of genomic sources. Finally, we describe the validation of the conceptual model by mapping it to three well-known data sources: TCGA, ENCODE, and Gene Expression Omnibus

On systematic approaches for interpreted information transfer of inspection data from bridge models to structural analysis

In conjunction with the improved methods of monitoring damage and degradation processes, the interest in reliability assessment of reinforced concrete bridges is increasing in recent years. Automated imagebased inspections of the structural surface provide valuable data to extract quantitative information about deteriorations, such as crack patterns. However, the knowledge gain results from processing this information in a structural context, i.e. relating the damage artifacts to building components. This way, transformation to structural analysis is enabled. This approach sets two further requirements: availability of structural bridge information and a standardized storage for interoperability with subsequent analysis tools. Since the involved large datasets are only efficiently processed in an automated manner, the implementation of the complete workflow from damage and building data to structural analysis is targeted in this work. First, domain concepts are derived from the back-end tasks: structural analysis, damage modeling, and life-cycle assessment. The common interoperability format, the Industry Foundation Class (IFC), and processes in these domains are further assessed. The need for usercontrolled interpretation steps is identified and the developed prototype thus allows interaction at subsequent model stages. The latter has the advantage that interpretation steps can be individually separated into either a structural analysis or a damage information model or a combination of both. This approach to damage information processing from the perspective of structural analysis is then validated in different case studies

Towards Designing and Generating User Interfaces by Using Expert Knowledge

[ES] La investigación reportada en la presente tesis doctoral se lleva a cabo a través de la metodología de la ciencia del diseño que se centra en la creación y evaluación de artefactos. En esta tesis, el principal artefacto es el novedoso enfoque para diseñar y generar interfaces de usuario utilizando el conocimiento experto. Con el fin de permitir el uso del conocimiento experto, el enfoque propuesto se basa en la reutilización de patrones de diseño que incorporan el conocimiento experto del diseño de la interfaz y proporcionan soluciones reutilizables a diversos problemas de diseño. El objetivo principal de dicho enfoque es abordar el uso de patrones de diseño a fin de garantizar que los conocimientos especializados se integren en el diseño y la generación de interfaces de usuario para aplicaciones móviles y web. Las contribuciones específicas de esta tesis se resumen a continuación: Una primera contribución consiste en el marco AUIDP que se define para apoyar el diseño y la generación de interfaces adaptativas para aplicaciones web y móviles utilizando patrones de diseño HCI. El marco propuesto abarca tanto la etapa de diseño como la de ejecución de dichas interfaces. En el momento del diseño, los modelos de patrones de diseño junto con la interfaz de usuario y el perfil de usuario se definen siguiendo una metodología de desarrollo específica. En tiempo de ejecución, los modelos creados se utilizan para permitir la selección de patrones de diseño de HCI y para permitir la generación de interfaces de usuario a partir de las soluciones de diseño proporcionadas por los patrones de diseño relevantes. La segunda contribución es un método de especificación para establecer un modelo de ontología que convierte la representación tradicional basada en texto en la representación formal del patrón de diseño de HCI. Este método adopta la metodología Neon para lograr la transición de las representaciones informales a las formales. El modelo de ontología creado se llama MIDEP, que es una ontología modular que captura el conocimiento sobre los patrones de diseño, así como la interfaz de usuario y el perfil del usuario. La tercera contribución es el IDEPAR, que es el primer sistema dentro del marco global del AUIDP. Este sistema tiene como objetivo recomendar automáticamente los patrones de diseño más relevantes para un problema de diseño dado. Se basa en un enfoque híbrido que utiliza una combinación mixta de técnicas de recomendación basadas en texto y ontología para producir recomendaciones de patrones de diseño que proporcionan soluciones de diseño apropiadas. La cuarta contribución es un sistema generador de interfaz llamado ICGDEP, que se propone para generar automáticamente el código fuente de la interfaz de usuario para aplicaciones web y móviles. El ICGDEP es el segundo sistema dentro del marco global de AUIDP y se basa en el uso de patrones de diseño de HCI que son recomendados por el sistema IDEPAR. Su objetivo principal es generar automáticamente el código fuente de la interfaz de usuario a partir de las soluciones de diseño proporcionadas por los patrones de diseño. Para lograr esto, el sistema ICGDEP utiliza un método que permite la generación de código fuente de interfaz de usuario para la aplicación de destino. Las contribuciones aportadas en la presente tesis han sido validadas a través de diferentes perspectivas. En primer lugar, la evaluación de la ontología MIDEP desarrollada se realiza utilizando preguntas de competencia, enfoques de evaluación basados en la tecnología y basados en aplicaciones. En segundo lugar, la evaluación del sistema IDEPAR se establece mediante un patrón producido por expertos y un estudio de evaluación centrado en el usuario. Luego, el sistema ICGDEP es evaluado en términos de ser utilizado efectivamente por los desarrolladores, considerando el factor de productividad. Por último, la evaluación del marco mundial de AUIDP se lleva a cabo mediante estudios de casos y estudios de usabilidad.[CA] La investigació reportada en aquesta tesi doctoral es duu a terme a través de la metodologia de la ciència del disseny que se centra en la creació i avaluació d'artefactes. En aquesta tesi, el principal artefacte és el nou enfocament per dissenyar i generar interfícies d'usuari utilitzant el coneixement expert. Per tal de permetre l'ús del coneixement expert, l'enfocament proposat es basa en la reutilització de patrons de disseny que incorporen el coneixement expert del disseny de la interfície i proporcionen solucions reutilitzables a diversos problemes de disseny. L'objectiu principal d'aquest enfocament és abordar l'ús de patrons de disseny per tal de garantir que els coneixements especialitzats s'integrin en el disseny i la generació d'interfícies d'usuari per a aplicacions mòbils i web. Les contribucions específiques d'aquesta tesi es resumeixen a continuació: Una primera contribució consisteix en el marc AUIDP que es defineix per donar suport al disseny i generació d'interfícies adaptatives per a aplicacions web i mòbils utilitzant patrons de disseny HCI. El marc proposat inclou tant l'etapa de disseny com la d'execució de les interfícies esmentades. En el moment del disseny, els models de patrons de disseny juntament amb la interfície d'usuari i el perfil d'usuari es defineixen seguint una metodologia de desenvolupament específica. En temps d'execució, els models creats s'utilitzen per permetre la selecció de patrons de disseny de HCI i per permetre la generació de interfícies d'usuari a partir de les solucions de disseny proporcionades pels patrons de disseny rellevants. La segona contribució és un mètode d'especificació per establir un model d'ontologia que converteix la representació tradicional basada en text en la representació formal del patró de disseny de HCI. Aquest mètode adopta la metodologia Neon per aconseguir la transició de les representacions informals a les formals. El model d'ontologia creat s'anomena MIDEP, una ontologia modular que captura el coneixement sobre els patrons de disseny, així com la interfície d'usuari i el perfil de l'usuari. La tercera contribució és l'IDEPAR, que és el primer sistema dins del marc global de l'AUIDP. Aquest sistema té com a objectiu recomanar automàticament els patrons de disseny més rellevants per a un problema de disseny donat. Es basa en un enfocament híbrid que utilitza una combinació mixta de tècniques de recomanació basades en text i ontologia per produir recomanacions de patrons de disseny que proporcionen solucions de disseny apropiades. La quarta contribució és un sistema generador d'interfície anomenat ICGDEP, que es proposa per generar automàticament el codi font de la interfície d'usuari per a aplicacions web i mòbils. L'ICGDEP és el segon sistema dins del marc global d'AUIDP i es basa en l'ús de patrons de disseny de HCI que són recomanats pel sistema IDEPAR. El seu objectiu principal és generar automàticament el codi font de la interfície d'usuari a partir de les solucions de disseny proporcionades pels patrons de disseny. Per aconseguir-ho, el sistema ICGDEP utilitza un mètode que permet generar codi font d'interfície d'usuari per a l'aplicació de destinació. Les contribucions aportades a la present tesi han estat validades a través de diferents perspectives. En primer lloc, l'avaluació de l'ontologia MIDEP desenvolupada es fa utilitzant preguntes de competència, enfocaments d'avaluació basats en la tecnologia i basats en aplicacions. En segon lloc, l'avaluació del sistema IDEPAR s'estableix mitjançant un patró produït per experts i un estudi d'avaluació centrat en l'usuari. Després, el sistema ICGDEP és avaluat en termes de ser utilitzat efectivament pels desenvolupadors, considerant el factor de productivitat. Finalment, l'avaluació del marc mundial d'AUIDP es fa mitjançant estudis de casos i estudis d'usabilitat.[EN] The research reported in the present PhD dissertation is conducted through the design science methodology that focuses on creating and evaluating artifacts. In the current thesis, the main artifact is the novel approach to design and generate user interfaces using expert knowledge. In order to enable the use of expert knowledge, the present approach is devoted to reuse design patterns that incorporate expert knowledge of interface design and provide reusable solutions to various design problems. The main goal of the proposed approach is to address the use of design patterns in order to ensure that expert knowledge is integrated into the design and generation of user interfaces for mobile and Web applications. The specific contributions of this thesis are summarized below: This first contribution is the AUIDP framework that is defined to support the design and generation of adaptive interfaces for Web and mobile applications using HCI design patterns. The proposed framework spans over design-time and run-time. At design-time, models of design patterns along with user interface and user profile are defined following a specific development methodology. At run-time, the created models are used to allow the selection of HCI design patterns and to enable the generation of user interfaces from the design solutions provided by the relevant design patterns. The second contribution is a specification method to establish an ontology model that turns traditional text-based representation into formal HCI design pattern representation. This method adopts the Neon methodology to achieve the transition from informal to formal representations. The created ontology model is named MIDEP, which is a modular ontology that captures knowledge about design patterns as well as the user interface and user's profile. The third contribution is the IDEPAR, which is the first system within the global AUIDP framework. This system aims to automatically recommend the most relevant design patterns for a given design problem. It is based on a hybrid approach that relies on a mixed combination of text-based and ontology-based recommendation techniques to produce design pattern recommendations that provide appropriate design solutions. The fourth contribution is an interface generator system called ICGDEP, which is proposed to automatically generate the user interface source code for Web and mobile applications. The proposed ICGDEP is the second system within the global AUIDP framework and relies on the use of HCI design patterns that are recommended by the IDEPAR system. It mainly aims at automatically generating the user interface source code from the design solutions provided by design patterns. To achieve this, the ICGDEP system is based on a generation method that allows the generation of user interface source code for the target application. The contributions provided in the present thesis have been validated through different perspectives. First, the evaluation of the developed MIDEP ontology is performed using competency questions, technology-based, and application-based evaluation approaches. Second, the evaluation of the IDEPAR system is established through an expert-based gold standard and a user-centric evaluation study. Then, the ICGDEP system is evaluated in terms of being effectively used by developers, considering the productivity factor. Finally, the evaluation of the global AUIDP framework is conducted through case studies and usability studies.Braham, A. (2022). Towards Designing and Generating User Interfaces by Using Expert Knowledge [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/19092

B!SON: A Tool for Open Access Journal Recommendation

Finding a suitable open access journal to publish scientific work is a complex task: Researchers have to navigate a constantly growing number of journals, institutional agreements with publishers, funders’ conditions and the risk of Predatory Publishers. To help with these challenges, we introduce a web-based journal recommendation system called B!SON. It is developed based on a systematic requirements analysis, built on open data, gives publisher-independent recommendations and works across domains. It suggests open access journals based on title, abstract and references provided by the user. The recommendation quality has been evaluated using a large test set of 10,000 articles. Development by two German scientific libraries ensures the longevity of the project

BlogForever D3.2: Interoperability Prospects

This report evaluates the interoperability prospects of the BlogForever platform. Therefore, existing interoperability models are reviewed, a Delphi study to identify crucial aspects for the interoperability of web archives and digital libraries is conducted, technical interoperability standards and protocols are reviewed regarding their relevance for BlogForever, a simple approach to consider interoperability in specific usage scenarios is proposed, and a tangible approach to develop a succession plan that would allow a reliable transfer of content from the current digital archive to other digital repositories is presented

Semantic-guided predictive modeling and relational learning within industrial knowledge graphs

The ubiquitous availability of data in today’s manufacturing environments, mainly driven by the extended usage of software and built-in sensing capabilities in automation systems, enables companies to embrace more advanced predictive modeling and analysis in order to optimize processes and usage of equipment. While the potential insight gained from such analysis is high, it often remains untapped, since integration and analysis of data silos from different production domains requires high manual effort and is therefore not economic. Addressing these challenges, digital representations of production equipment, so-called digital twins, have emerged leading the way to semantic interoperability across systems in different domains. From a data modeling point of view, digital twins can be seen as industrial knowledge graphs, which are used as semantic backbone of manufacturing software systems and data analytics. Due to the prevalent historically grown and scattered manufacturing software system landscape that is comprising of numerous proprietary information models, data sources are highly heterogeneous. Therefore, there is an increasing need for semi-automatic support in data modeling, enabling end-user engineers to model their domain and maintain a unified semantic knowledge graph across the company. Once the data modeling and integration is done, further challenges arise, since there has been little research on how knowledge graphs can contribute to the simplification and abstraction of statistical analysis and predictive modeling, especially in manufacturing. In this thesis, new approaches for modeling and maintaining industrial knowledge graphs with focus on the application of statistical models are presented. First, concerning data modeling, we discuss requirements from several existing standard information models and analytic use cases in the manufacturing and automation system domains and derive a fragment of the OWL 2 language that is expressive enough to cover the required semantics for a broad range of use cases. The prototypical implementation enables domain end-users, i.e. engineers, to extend the basis ontology model with intuitive semantics. Furthermore it supports efficient reasoning and constraint checking via translation to rule-based representations. Based on these models, we propose an architecture for the end-user facilitated application of statistical models using ontological concepts and ontology-based data access paradigms. In addition to that we present an approach for domain knowledge-driven preparation of predictive models in terms of feature selection and show how schema-level reasoning in the OWL 2 language can be employed for this task within knowledge graphs of industrial automation systems. A production cycle time prediction model in an example application scenario serves as a proof of concept and demonstrates that axiomatized domain knowledge about features can give competitive performance compared to purely data-driven ones. In the case of high-dimensional data with small sample size, we show that graph kernels of domain ontologies can provide additional information on the degree of variable dependence. Furthermore, a special application of feature selection in graph-structured data is presented and we develop a method that allows to incorporate domain constraints derived from meta-paths in knowledge graphs in a branch-and-bound pattern enumeration algorithm. Lastly, we discuss maintenance of facts in large-scale industrial knowledge graphs focused on latent variable models for the automated population and completion of missing facts. State-of-the art approaches can not deal with time-series data in form of events that naturally occur in industrial applications. Therefore we present an extension of learning knowledge graph embeddings in conjunction with data in form of event logs. Finally, we design several use case scenarios of missing information and evaluate our embedding approach on data coming from a real-world factory environment. We draw the conclusion that industrial knowledge graphs are a powerful tool that can be used by end-users in the manufacturing domain for data modeling and model validation. They are especially suitable in terms of the facilitated application of statistical models in conjunction with background domain knowledge by providing information about features upfront. Furthermore, relational learning approaches showed great potential to semi-automatically infer missing facts and provide recommendations to production operators on how to keep stored facts in synch with the real world

Linked Open Data - Creating Knowledge Out of Interlinked Data: Results of the LOD2 Project

Database Management; Artificial Intelligence (incl. Robotics); Information Systems and Communication Servic

31th International Conference on Information Modelling and Knowledge Bases

Information modelling is becoming more and more important topic for researchers, designers, and users of information systems.The amount and complexity of information itself, the number of abstractionlevels of information, and the size of databases and knowledge bases arecontinuously growing. Conceptual modelling is one of the sub-areas ofinformation modelling. The aim of this conference is to bring together experts from different areas of computer science and other disciplines, who have a common interest in understanding and solving problems on information modelling and knowledge bases, as well as applying the results of research to practice. We also aim to recognize and study new areas on modelling and knowledge bases to which more attention should be paid. Therefore philosophy and logic, cognitive science, knowledge management, linguistics and management science are relevant areas, too. In the conference, there will be three categories of presentations, i.e. full papers, short papers and position papers