A method to generate a modular ifcOWL ontology

Building Information Modeling (BIM) and Semantic Web technologies are becoming more and more popular in the Architecture Engineering Construction (AEC) and Facilities Management (FM) industry to support information management, information exchange and data interoperability. One of the key integration gateways between BIM and Semantic Web is represented by the ifcOWL ontology, i.e. the Web Ontology Language (OWL) version of the IFC standard, being one of reference technical standard for AEC/FM. Previous studies have shown how a recommended ifcOWL ontology can be automatically generated by converting the IFC standard from the official EXPRESS schema. However, the resulting ifcOWL is a large monolithic ontology that presents serious limitations for real industrial applications in terms of usability and performance (i.e. querying and reasoning). Possible enhancements to reduce the complexity and the data size consist in (1) modularization of ifcOWL making it easier to use subsets of the entire ontology, and (2) rethinking the contents and structure of an ontology for AEC/FM to better fit in the semantic web scope and make its usage more efficient. The second approach can be enabled by the first one, since it would make it easier to replace some of the ifcOWL modules with new optimized ontologies for the AEC-FM industry. This paper focuses on the first approach presenting a method to automatically generate a modular ifcOWL ontology. The method aims at minimizing the dependencies between modules to better exploit the modularization. The results are compared with simpler and more straight-forward solutions

A Query Integrator and Manager for the Query Web

We introduce two concepts: the Query Web as a layer of interconnected queries over the document web and the semantic web, and a Query Web Integrator and Manager (QI) that enables the Query Web to evolve. QI permits users to write, save and reuse queries over any web accessible source, including other queries saved in other installations of QI. The saved queries may be in any language (e.g. SPARQL, XQuery); the only condition for interconnection is that the queries return their results in some form of XML. This condition allows queries to chain off each other, and to be written in whatever language is appropriate for the task. We illustrate the potential use of QI for several biomedical use cases, including ontology view generation using a combination of graph-based and logical approaches, value set generation for clinical data management, image annotation using terminology obtained from an ontology web service, ontology-driven brain imaging data integration, small-scale clinical data integration, and wider-scale clinical data integration. Such use cases illustrate the current range of applications of QI and lead us to speculate about the potential evolution from smaller groups of interconnected queries into a larger query network that layers over the document and semantic web. The resulting Query Web could greatly aid researchers and others who now have to manually navigate through multiple information sources in order to answer specific questions

OntoFox: web-based support for ontology reuse

<p>Abstract</p> <p>Background</p> <p>Ontology development is a rapidly growing area of research, especially in the life sciences domain. To promote collaboration and interoperability between different projects, the OBO Foundry principles require that these ontologies be open and non-redundant, avoiding duplication of terms through the re-use of existing resources. As current options to do so present various difficulties, a new approach, MIREOT, allows specifying import of single terms. Initial implementations allow for controlled import of selected annotations and certain classes of related terms.</p> <p>Findings</p> <p>OntoFox <url>http://ontofox.hegroup.org/</url> is a web-based system that allows users to input terms, fetch selected properties, annotations, and certain classes of related terms from the source ontologies and save the results using the RDF/XML serialization of the Web Ontology Language (OWL). Compared to an initial implementation of MIREOT, OntoFox allows additional and more easily configurable options for selecting and rewriting annotation properties, and for inclusion of all or a computed subset of terms between low and top level terms. Additional methods for including related classes include a SPARQL-based ontology term retrieval algorithm that extracts terms related to a given set of signature terms and an option to extract the hierarchy rooted at a specified ontology term. OntoFox's output can be directly imported into a developer's ontology. OntoFox currently supports term retrieval from a selection of 15 ontologies accessible via SPARQL endpoints and allows users to extend this by specifying additional endpoints. An OntoFox application in the development of the Vaccine Ontology (VO) is demonstrated.</p> <p>Conclusions</p> <p>OntoFox provides a timely publicly available service, providing different options for users to collect terms from external ontologies, making them available for reuse by import into client OWL ontologies.</p

The relevance of perspectivism to the task of modularisation in ontology development

An ontology development methodology seeks to provide developers with established principles, processes, practices, methods and activities for developing ontologies (Gasevic et al., 2009). Diverse methodologies have been published for the development of ontologies, and have evolved, based on the diverse experiences of researchers and practitioners, and the development teams who surveyed the benefits and shortcomings of the available methodologies in order to determine the applicability of methodologies to particular contexts. An evaluation of existing ontology development methodologies has identified that the concept formulation process is not well defined, or based on rigorous processes (Castro et al., 2006; Winters &amp; Tolk, 2009). In order for the validity of the social realism of the actors in a social setting to be captured, the perspectives of each actor needs to be acknowledged and incorporated into the concept formulation process / framework. This paper demonstrates how consideration of perspectivism leads to a meaningful modularisation of the resultant ontology.<br /

OntoVIP: An ontology for the annotation of object models used for medical image simulation.

International audienceThis paper describes the creation of a comprehensive conceptualization of object models used in medical image simulation, suitable for major imaging modalities and simulators. The goal is to create an application ontology that can be used to annotate the models in a repository integrated in the Virtual Imaging Platform (VIP), to facilitate their sharing and reuse. Annotations make the anatomical, physiological and pathophysiological content of the object models explicit. In such an interdisciplinary context we chose to rely on a common integration framework provided by a foundational ontology, that facilitates the consistent integration of the various modules extracted from several existing ontologies, i.e. FMA, PATO, MPATH, RadLex and ChEBI. Emphasis is put on methodology for achieving this extraction and integration. The most salient aspects of the ontology are presented, especially the organization in model layers, as well as its use to browse and query the model repository

OntoVIP: An ontology for the annotation of object models used for medical image simulation

This paper describes the creation of a comprehensive conceptualization of object models used in medical image simulation, suitable for major imaging modalities and simulators. The goal is to create an application ontology that can be used to annotate the models in a repository integrated in the Virtual Imaging Platform (VIP), to facilitate their sharing and reuse. Annotations make the anatomical, physiological and pathophysiological content of the object models explicit. In such an interdisciplinary context we chose to rely on a common integration framework provided by a foundational ontology, that facilitates the consistent integration of the various modules extracted from several existing ontologies, i.e. FMA, PATO, MPATH, RadLex and ChEBI. Emphasis is put on methodology for achieving this extraction and integration. The most salient aspects of the ontology are presented, especially the organization in model layers, as well as its use to browse and query the model repository

Implementing electronic scales to support standardized phenotypic data collection - the case of the Scale for the Assessment and Rating of Ataxia (SARA)

The main objective of this doctoral thesis was to facilitate the integration of the semantics required to automatically interpret collections of standardized clinical data. In order to address the objective, we combined the best performances from clinical archetypes, guidelines and ontologies for developing an electronic prototype for the Scale of the Assessment and Rating of Ataxia (SARA), broadly used in neurology. A scaled-down version of the Human Phenotype Ontology was automatically extracted and used as backbone to normalize the content of the SARA through clinical archetypes. The knowledge required to exploit reasoning on the SARA data was modeled as separate information-processing units interconnected via the defined archetypes. Based on this approach, we implemented a prototype named SARA Management System, to be used for both the assessment of cerebellar syndrome and the production of a clinical synopsis. For validation purposes, we used recorded SARA data from 28 anonymous subjects affected by SCA36. Our results reveal a substantial degree of agreement between the results achieved by the prototype and human experts, confirming that the combination of archetypes, ontologies and guidelines is a good solution to automate the extraction of relevant phenotypic knowledge from plain scores of rating scales

Desarrollo e integración de ontologías para la representación de pacientes COVID-19

Desde que se identificó el primer caso conocido de síndrome respiratorio agudo severo coronavirus 2 (SARS-CoV-2) en Wuhan, China, en diciembre de 2019, la enfermedad se ha extendido por todo el mundo, dando lugar a la pandemia actual de COVID-19 que causa síntomas variables en las personas, pero a menudo incluyen fiebre, tos seca, dolor de cabeza, fatiga, dificultad para respirar, perdida del olfato y perdida del gusto, de aquellos que desarrollan síntomas lo suficientemente notables como para ser clasificados como pacientes, la mayoría desarrolla síntomas leves a moderados, mientras que el 14% desarrolla síntomas graves y el 5% sufre síntomas críticos, por lo que deben ser atendidos en centros especializados. De modo que cada paciente reacciona de forma diferente ante la enfermedad, el saber cuáles son los factores que determinan la condición de un paciente es importante para conocer el comportamiento de la enfermedad, por ello se considera implementar bases de conocimientos que permitan generar nuevo conocimiento en base a los hechos establecidos de la información de pacientes, por ejemplo, el tratamiento de los pacientes. Por tal motivo, en este proyecto de investigación se reporta el diseño y desarrollo un sistema de modelos ontológicos integrados para la representación y administración de perfiles de pacientes con COVID-19: diagnóstico y tratamiento, donde los resultados de las diferentes evaluaciones realizadas a dicho modelo, muestran la factibilidad de utilizar esta base de conocimiento integrada por ontologías para la representación de la información clínica del paciente y obtener nuevo conocimiento y servir como base para proyectos de investigación a futuro.Since the first known case of severe acute respiratory syndrome coronavirus 2 (SARSCoV- 2) was identified in Wuhan, China, in December 2019, the disease has spread throughout the world, giving rise to the current COVID pandemic. -19 which causes variable symptoms in people, but often include fever, dry cough, headache, fatigue, shortness of breath, loss of smell, and loss of taste, of those who develop symptoms notable enough to be classified as patients, the majority develop mild to moderate symptoms, while 14% develop severe symptoms and 5% suffer critical symptoms, so they must be treated in specialized centers. So that each patient reacts differently to the disease, adequate knowledge of the factors that determine the condition of a patient is important to know the behavior of the disease, for this reason, it is considered to implement knowledge bases that allow generating new knowledge is based on established facts of patient information, for example, treatment of patients. For this reason, this research project reports the design and development of a system of integrated ontological models for the representation and administration of profiles of patients with COVID-19: diagnosis and treatment, where the results of the different evaluations carried out on the said model, show the feasibility of using this knowledge base integrated by ontologies to represent the patient’s clinical information and obtain new knowledge and serve as a basis for future research projects