2020
Time event ontology (TEO): to support semantic representation and reasoning of complex temporal relations of clinical events
Li F, Du J, He Y, Song H, Madkour M, Rao G, Xiang Y, Luo Y, Chen H, Liu S, Wang L, Liu H, Xu H, Tao C. Time event ontology (TEO): to support semantic representation and reasoning of complex temporal relations of clinical events. Journal Of The American Medical Informatics Association 2020, 27: 1046-1056. PMID: 32626903, PMCID: PMC7647306, DOI: 10.1093/jamia/ocaa058.Peer-Reviewed Original ResearchConceptsTime Event OntologyComplex temporal relationsEvent ontologyNatural language processing fieldTemporal relationsTime-related queriesInformation annotationProcessing fieldTemporal informationData propertiesRelation representationClinical narrativesSemantic representationElectronic health record dataRich setHealth record dataOntologyStrong capabilityReasoningSetQueriesOrder relationRecord dataRepresentationPrimitives
2016
Leveraging syntactic and semantic graph kernels to extract pharmacokinetic drug drug interactions from biomedical literature
Zhang Y, Wu H, Xu J, Wang J, Soysal E, Li L, Xu H. Leveraging syntactic and semantic graph kernels to extract pharmacokinetic drug drug interactions from biomedical literature. BMC Systems Biology 2016, 10: 67. PMID: 27585838, PMCID: PMC5009562, DOI: 10.1186/s12918-016-0311-2.Peer-Reviewed Original ResearchConceptsPaths graph kernelGraph kernelsSemantic classesSemantic informationBiomedical literatureShallow semantic representationsText mining techniquesBest F-scoreAutomatic DDI extractionProblem of sparsenessDependency structureSemantic graphDDI detectionKnowledge basesDDI corpusF-scoreDDI extractionSemantic representationNovel approachExperimental resultsKernelHigh precisionInformationSparsenessGraph