The MedRed Ontology for Representing Clinical Data Acquisition Metadata

Calbimonte, Jean-Paul; Dubosson, Fabien; Hilfiker, Roger; Cotting, Alexandre; Schumacher, Michael

doi:10.1007/978-3-319-68204-4_4

Cited by 5 publications

(7 citation statements)

References 11 publications

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“…The ontology reuses a number of existing ontologies (to increase interoperability); however, as reported in the related ISWC’17 paper (Peroni et al , 2017), there is no explicit information of its broad adoption worldwide so far, since it is has been recently released (2017). Having said that, United Nations and collaborating agencies will use it for describing their documents and their interrelations in RDF.The MedRed ontology (Calbimonte et al , 2017) ( ) has been developed in the context of the MedRed acquisition project (MedRed: ), with the goal of representing clinical data acquisition metadata. The ontology is live (the latest version of MedRed ontology is available at ) and online for others to reuse under the licence CC-BY 4.0.…”

Section: Selected Ontologiesmentioning

confidence: 99%

“…SO7. The MedRed ontology (Calbimonte et al, 2017) (http://w3id.org/medred/medred#) has been developed in the context of the MedRed acquisition project (MedRed: http://w3id.org/medred/ project), with the goal of representing clinical data acquisition metadata. The ontology is live (the latest version of MedRed ontology is available at https://jpcik.github.io/medred/onto/medred/ index-en.html) and online for others to reuse under the licence CC-BY 4.0.…”

Section: So1mentioning

confidence: 99%

“…Most of those approaches use Wiki and GitHub technology as a mean to support tool-based distributed collaboration in the development of commonly agreed ontologies. Representative approaches are included in Rebele et al (2016), Calbimonte et al (2017), Narula et al (2018), Salatino et al (2018), Tommasini et al (2018) and in the recent related work of Arndt et al (2019).…”

Section: Introductionmentioning

confidence: 99%

“…Representative approaches are included in Rebele et al (2016), Calbimonte et al (2017), Narula et al (2018), Salatino et al (2018), Tommasini et al (2018) and in the recent related work of Arndt et al (2019).…”

Section: Introductionmentioning

confidence: 99%

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Ontology engineering methodologies for the evolution of living and reused ontologies: status, trends, findings and recommendations

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2020

The Knowledge Engineering Review

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The aim of this critical review paper is threefold: (a) to provide an insight on the impact of ontology engineering methodologies (OEMs) to the evolution of living and reused ontologies, (b) to update the ontology engineering (OE) community on the status and trends in OEMs and of their use in practice and (c) to propose a set of recommendations for working ontologists to consider during the life cycle of living, evolved and reused ontologies. The work outlined in this critical review paper has been motivated by the need to address critical issues on keeping ontologies alive and evolving while these are shared in wide communities. It is argued that the engineering of ontologies must follow a well-defined methodology, addressing practical aspects that would allow (sometimes wide) communities of experts and ontologists to reach consensus on developments and keep the evolution of ontologies ‘in track’. In doing so, specific collaborative and iterative tool-supported tasks and phases within a complete and evaluated ontology life cycle are necessary. This way the engineered ontologies can be considered ‘shared, commonly agreed and continuously evolved “live” conceptualizations’ of domains of discourse. Today, in the era of Linked Data and Knowledge Graphs, it is more necessary than ever not to neglect to consider the recommendations that OEMs explicitly and implicitly introduce and their implications to the evolution of living ontologies. This paper reports on the status of OEMs, identifies trends and provides recommendations based on the findings of an analysis that concerns the impact of OEMs to the status of well-known, widely used and representative ontologies.

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Section: Selected Ontologiesmentioning

confidence: 99%

Section: So1mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Ontology engineering methodologies for the evolution of living and reused ontologies: status, trends, findings and recommendations

Kotis

Vouros

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The Knowledge Engineering Review

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“…Developers can thus build applications allowing users to securely create, manage and validate knowledge graphs. This component is based on a distributed and event streaming architecture, uniquely combining under a single API: a scalable data store using Cassandra, 10 a flexible RDF 11 -based graph store using Blazegraph 12 and a powerful full-text search engine using Elasticsearch. 13 Nexus Delta enables high-level, declarative and expressive metadata representations and validation while still being able to turn them into a developer-friendly and secured RESTful API.…”

Section: Servicesmentioning

confidence: 99%

Blue Brain Nexus: An open, secure, scalable system for knowledge graph management and data-driven science

Roman

Kerrien

et al. 2023

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Modern data-driven science often consists of iterative cycles of data discovery, acquisition, preparation, analysis, model building and validation leading to knowledge discovery as well as dissemination at scale. The unique challenges of building and simulating the whole rodent brain in the Swiss EPFL Blue Brain Project (BBP) required a solution to managing large-scale highly heterogeneous data, and tracking their provenance to ensure quality, reproducibility and attribution throughout these iterative cycles. Here, we describe Blue Brain Nexus (BBN), an ecosystem of open source, domain agnostic, scalable, extensible data and knowledge graph management systems built by BBP to address these challenges. BBN builds on open standards and interoperable semantic web technologies to enable the creation and management of secure RDF-based knowledge graphs validated by W3C SHACL. BBN supports a spectrum of (meta)data modeling and representation formats including JSON and JSON-LD as well as more formally specified SHACL-based schemas enabling domain model-driven runtime API. With its streaming event-based architecture, BBN supports asynchronous building and maintenance of multiple extensible indices to ensure high performance search capabilities and enable analytics. We present four use cases and applications of BBN to large-scale data integration and dissemination challenges in computational modeling, neuroscience, psychiatry and open linked data.

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SanTour: Towards Personalized Recommendation of Hiking Trails to Health Profiles

Calbimonte

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et al. 2018

Current Trends in Web Engineering

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The MedRed Ontology for Representing Clinical Data Acquisition Metadata

Cited by 5 publications

References 11 publications

Ontology engineering methodologies for the evolution of living and reused ontologies: status, trends, findings and recommendations

Ontology engineering methodologies for the evolution of living and reused ontologies: status, trends, findings and recommendations

Blue Brain Nexus: An open, secure, scalable system for knowledge graph management and data-driven science

SanTour: Towards Personalized Recommendation of Hiking Trails to Health Profiles

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