Translational research combining orthologous genes and human diseases with the OGOLOD dataset

OGOLOD is a Linked Open Data dataset derived from different biomedical resources by an automated pipeline, using a tailored ontology as a scaffold. The key contribution of OGOLOD is that it links, in new RDF triples, genetic human diseases and orthologous genes, paving the way for a more efficient translational biomedical research exploiting the Linked Open Data cloud

A systematic literature review on the development and use of mobile learning (web) apps by early adopters

Surveys in mobile learning developed so far have analysed in a global way the effects on the usage of mobile devices by means of general apps or apps already developed. However, more and more teachers are developing their own apps to address issues not covered by existing m-learning apps. In this article, by means of a systematic literature review that covers 62 publications placed in the hype of teacher-created m-learning apps (between 2012 and 2017, the early adopters) and the usage of 71 apps, we have analysed the use of specific m-learning apps. Our results show that apps have been used both out of the classroom to develop autonomous learning or field trips, and in the classroom, mainly, for collaborative activities. The experiences analysed only develop low level outcomes and the results obtained are positive improving learning, learning performance, and attitude. As a conclusion of this study is that the results obtained with specific developed apps are quite similar to previous general surveys and that the development of long-term experiences are required to determine the real effect of instructional designs based on mobile devices. These designs should also be oriented to evaluate high level skills and take advantage of mobile features of mobile devices to develop learning activities that be made anytime at anyplace and taking into account context and realistic situations. Furthermore, it is considered relevant the study of the role of educational mobile development frameworks in facilitating teachers the development of m-learning apps

A METHODOLOGY FOR ONTOLOGICAL KNOWLEDGE CAPTURE FROM DATABASES \ud

The successful emergence of the Information and Communication Technologies (ICT) has contributed to the efficiency improvement in a number of economic sectors. However, some strategic economic sectors, such as construction, have not been targeted enough yet. Construction-related ICT solutions lack mechanisms to permit the effective integration of the whole supply chain. Semantic Web can tackle these issues. This paper presents a methodology for acquiring knowledge from construction-related databases. A domain ontology has been developed that contains the relevant concepts regarding supply management in the construction domain. The methodology basically consists of mapping the database content onto the ontology and a further this one’s population by applying a set of mapping rules.\ud Успешное появление информационно-коммуникационных технологий (ИКТ) внесло свой вклад в повышение эффективности многих секторов экономики. Однако, некоторые стратегические экономические сектора, такие, как строительство, не были все же достаточно исследованы. Связанные со строительством решения ИКТ испытывают недостаток в механизмах, позволяющих разрешать проблемы эффективной интеграции полной цепочки поставки. Семантическая Сеть может заняться этими проблемами. Эта статья представляет методологию, позволяющую извлекать знание из баз данных, связанных со строительством . Была разработана онтология домена, содержащая релевантные понятия, касающиеся управления поставками в домене строительства. Методология в основном состоит из отображения содержания базы данных на онтологию и дальнейшего ее заполнения , применяя набор правил отображения .\u

Obteniendo respuestas de repositorios semánticos usando palabras clave

The Web of Data proposes to publish and connect data by applying the semantic web technologies for the representation of knowledge and data and the definition of queries. The success of the Web of Data requires that both humans and machines are able to extract information from such semantic repositories. For this purpose, query interfaces for humans must make the interaction with the repository as transparent as possible. In this work, we present a generic method for querying semantic repositories based on processing and recognizing the keywords input by the users as entities of the ontology used in the description of the data. A SPARQL query is automatically derived from the query graph extracted from the list of keywords. We also describe the application of the method to three different semantic repositories from different domains.La Web de Datos propone publicar y conectar los datos utilizando las tecnologías de la web semántica para la representación del conocimiento, de los datos y la especificación de consultas. El éxito de la Web de Datos requiere que tanto los humanos como las máquinas sean capaces de obtener información en estos repositorios semánticos. Para ello, los interfaces de consulta para humanos deben hacer lo más transparente posible el proceso de interacción con el repositorio. En este trabajo presentamos un método genérico para la consulta de repositorios semánticos basado en el reconocimiento de las keywords introducidas por los usuarios como entidades de la ontología utilizada para la descripción de los datos. Del procesamiento del grafo de consulta generado se deriva automáticamente la consulta SPARQL que se ejecuta contra el repositorio. Describimos el uso del método con tres repositorios de distintos dominios.This work has been funded by the Spanish Ministry of Economy, Industry and Competitiveness, the European Regional Development Fund (ERDF) Programme and the Fundación Séneca through grants TIN2014-53749-C2-2-R and 19371/PI/14

Publishing Orthology and Diseases Information in the Linked Open Data Cloud

The Linked Data initiative offers a straight method to publish structured data in the World Wide Web and link it to other data, resulting in a world wide network of semantically codified data known as the Linked Open Data cloud. The size of the Linked Open Data cloud, i.e. the amount of data published using Linked Data principles, is growing exponentially, including life sciences data. However, key information for biological research is still missing in the Linked Open Data cloud. For example, the relation between orthologs genes and genetic diseases is absent, even though such information can be used for hypothesis generation regarding human diseases. The OGOLOD system, an extension of the OGO Knowledge Base, publishes orthologs/diseases information using Linked Data. This gives the scientists the ability to query the structured information in connection with other Linked Data and to discover new information related to orthologs and human diseases in the cloud

Towards the semantic enrichment of Computer Interpretable Guidelines: a method for the identification of relevant ontological terms

Ponència presentada a 2018 The American Medical Informatics Association Annual Symposium (AMIA 2018) celebrat a San Francisco, Estats Units de l'Amèrica del Nord, el 3 de novembre de 2018Clinical Practice Guidelines (CPGs) contain recommendations intended to optimize patient care, produced based on a systematic review of evidence. In turn, Computer-Interpretable Guidelines (CIGs) are formalized versions of CPGs for use as decision-support systems. We consider the enrichment of the CIG by means of an OWL ontology that describes the clinical domain of the CIG, which could be exploited e.g. for the interoperability with the Electronic Health Record (EHR). As a first step, in this paper we describe a method to support the development of such an ontology starting from a CIG. The method uses an alignment algorithm for the automated identification of ontological terms relevant to the clinical domain of the CIG, as well as a web platform to manually review the alignments and select the appropriate ones. Finally, we present the results of the application of the method to a small corpus of CIGs

AN ONTOLOGY-BASED APPROACH TO KNOWLEDGE ACQUISITION FROM TEXT

Knowledge extraction from texts corpora can simplify the knowledge acquisition process as the participation of knowledge engineers would not be required and systems would acquisition and natural language recognition research areas for acquiring knowledge from text. The knowledge acquisition process, which represented by means of ontologies, is described an this paper as well as the validation of the tool in a specific linguistic domain.La extracción de conocimiento a partir de textos corpus lingüísticos puede simplificar el proceso de adquisición de conocimiento, ya que no se precisará ingenieros del conocimiento y los sistemas informáticos podrán adquirir conocimiento directamente de expertos. Este trabajo presenta un sistema que integra procedimientos propios de las áreas de adquisición de conocimiento y de reconocimiento de lenguajes naturales. Además de la descripción del proceso de adquisición de conocimiento, representando mediante ontologías, se valida la herramienta implementada para un dominio lingüístico específico

OPPL-Galaxy, a Galaxy tool for enhancing ontology exploitation as part of bioinformatics workflows

Biomedical ontologies are key elements for building up the Life Sciences Semantic Web. Reusing and building biomedical ontologies requires flexible and versatile tools to manipulate them efficiently, in particular for enriching their axiomatic content. The Ontology Pre Processor Language (OPPL) is an OWL-based language for automating the changes to be performed in an ontology. OPPL augments the ontologists’ toolbox by providing a more efficient, and less error-prone, mechanism for enriching a biomedical ontology than that obtained by a manual treatment. Results We present OPPL-Galaxy, a wrapper for using OPPL within Galaxy. The functionality delivered by OPPL (i.e. automated ontology manipulation) can be combined with the tools and workflows devised within the Galaxy framework, resulting in an enhancement of OPPL. Use cases are provided in order to demonstrate OPPL-Galaxy’s capability for enriching, modifying and querying biomedical ontologies. Conclusions Coupling OPPL-Galaxy with other bioinformatics tools of the Galaxy framework results in a system that is more than the sum of its parts. OPPL-Galaxy opens a new dimension of analyses and exploitation of biomedical ontologies, including automated reasoning, paving the way towards advanced biological data analyses

A methodology for ontological knowledge capture from databases

The successful emergence of the Information and Communication Technologies (ICT) has contributed to the efficiency improvement in a number of economic sectors. However, some strategic economic sectors, such as construction, have not been targeted enough yet. Construction-related ICT solutions lack mechanisms to permit the effective integration of the whole supply chain. Semantic Web can tackle these issues. This paper presents a methodology for acquiring knowledge from construction-related databases. A domain ontology has been developed that contains the relevant concepts regarding supply management in the construction domain. The methodology basically consists of mapping the database content onto the ontology and a further this one’s population by applying a set of mapping rules.Успешное появление информационно-коммуникационных технологий (ИКТ) внесло свой вклад в повышение эффективности многих секторов экономики. Однако, некоторые стратегические экономические сектора, такие, как строительство, не были все же достаточно исследованы. Связанные со строительством решения ИКТ испытывают недостаток в механизмах, позволяющих разрешать проблемы эффективной интеграции полной цепочки поставки. Семантическая Сеть может заняться этими проблемами. Эта статья представляет методологию, позволяющую извлекать знание из баз данных, связанных со строительством . Была разработана онтология домена, содержащая релевантные понятия, касающиеся управления поставками в домене строительства. Методология в основном состоит из отображения содержания базы данных на онтологию и дальнейшего ее заполнения , применяя набор правил отображения

OntoEnrich: A platform for the lexical analysis of ontologies focused on their axiomatic enrichment

OntoEnrich es una plataforma online para la detección automática y análisis de regularidades léxicas encontradas en las etiquetas asociadas a los conceptos de una ontología. Un análisis guiado por estas regularidades permite explorar diferentes aspectos léxico/semánticos, como puede ser la aplicación de los principios del OBO Foundry en el caso de ontologías biomédicas. El objetivo de esta demostración es presentar casos de uso obtenidos al aplicar la herramienta en ontologías relevantes como Gene Ontology o SNOMED CT. Mostraremos cómo dicho análisis permite identificar semántica oculta a partir de contenido descrito en lenguaje natural (apto para humanos), y cómo podría ser usado para enriquecer la ontología creando nuevos axiomas lógicos (aptos para máquinas).We present OntoEnrich, an online platform for the automatic detection and guided analysis of lexical regularities in ontology labels. An analysis guided by these regularities permits users to explore different lexical and semantic aspects as the application of the OBO Foundry principles in biomedical ontologies. The goal of this demonstration is to show some use cases obtained after applying OntoEnrich in two relevant biomedical ontologies such as Gene Ontology and SNOMED CT. Thus, we will show how the performed analysis could be used to elucidate hidden semantics from the natural language fragments (human-friendly), and how this could be used to enrich the ontology by generating new logical axioms (machine-friendly).Este trabajo ha sido posible gracias al Ministerio de Economía y Competitividad y el Fondo Europeo de Desarrollo Regional (FEDER), a través del proyecto TIN2014-53749-C2-2-R, y a la Fundación Séneca a través del proyecto 19371/PI/14