Semantic interoperability

We have been working since 2004 in the management of EHR information and knowledge from a semantic web perspective with the aim of contributing to the achievement of semantic interoperability. Our research results include:

  • Poseacle converter: tool that permits the bi-directional transformation of archetypes and data between ISO 13606 and openEHR. Recently, the possibility of transforming CEM into openEHR archetypes has been added.
  • Archeck: Semantic validation of specialization in archetypes.
  • ArchMS: Ontology-based archetype management system for both ISO13606 and openEHR. It includes the Poseacle Converter and Archeck functionality, permits semantic annotation of archetypes, semantic searches, importing XML data extracts, recommendation of learning contents based on the EHR, classification of patients based on ontologies and reasoning, etc. It also uses our SWIT tool for the transformation of archetyped data into RDF/OWL format.

I have participated asexternal expert in the FP7 Network of Excellence SemanticHealthNet.

Semantic integration and generation of Open Data Sets

In the last years we have developed methods and tools for the ontology-driven integration and generation of data repositories, especially in the biomedical area. Our Semantic Web Integration tool (SWIT) permits the generation of semantically integrated RDF/OWL repositories. SWIT repositories are 5* according to Berners-Lee classification and follow the Linked Data principles.

These methods and tools have served to create our Linked Data Set OGOLOD, which integrates information about genetic disorders and orthology information. We are collaborating in the Quest for Orthologs consortium in the creation of ontologies and integrated repositories of orthology information.

We have also used these methods to obtain semantic representation of EHR data, chemical products or university open datasets.

Ontology Quality

eLearning and Training

  • We have developed an ontology-based platform of online assessment, called OeLE, which is able to mark answers provided in natural language to open questions by using domain knowledge. OeLE has been successfully applied in real courses. OeLE does not only provide the mark but is able to generate feedback to both students and teachers about what has to be reinforced by the students. Besides, the most recent version integrates a content repository, so the feedback may include specific reinforcement contents.
  • We have developed semantic methods for the automatic generation of training plans for workers. Such methods were based on the skills, the learning objectives of the workers, the skills required by her job position, and the goals of the company.
 
interests.txt · Última modificación: 27/08/2016 18:48 por jfernand@um.es
 
 
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