Marais, LaurettePretorius, L2021-03-072021-03-072021-02Marais, L. & Pretorius, L. 2021. Exploiting a multilingual semantic Machine translation architecture for knowledge representation of patient information for Covid-19. http://hdl.handle.net/10204/11827 .978-0-620-89373-2http://hdl.handle.net/10204/11827AwezaMed Covid-19 is a multilingual speech-to-speech translation application for screening patients for Covid-19. It enables Englishspeaking health care providers (HCPs) to conduct screenings by asking questions to patients in all other official languages of South-Africa. It uses a multimodal computational grammar translation system to enable English speech and screen-based input, which can be translated to produce synthetic speech in the target languages. Grammatical Framework is used for the translation system, utilising a semantic interlingua. Because of this, each utterance translated by the application is represented by a semantic tree, which could be exploited for knowledge representation. In this paper, we describe how the machine translation architecture designed for multilingual speech-to-speech translation can be adapted for knowledge representation consistent with existing knowledge representation formalisms, namely openEHR archetypes and RDF triples, which could be recorded seamlessly by HCPs during the screening.FulltextenCovid-19ScreeningopenEHRLinked dataConference PresentationMarais, L., & Pretorius, L. (2021). Exploiting a multilingual semantic Machine translation architecture for knowledge representation of patient information for Covid-19. http://hdl.handle.net/10204/11827Marais, Laurette, and L Pretorius. "Exploiting a multilingual semantic Machine translation architecture for knowledge representation of patient information for Covid-19." <i>Proceedings of the 1st South African Conference for Artificial Intelligence Research, (SACAIR 2020), Muldersdrift, South Africa, 22-26 February 2021</i> (2021): http://hdl.handle.net/10204/11827Marais L, Pretorius L, Exploiting a multilingual semantic Machine translation architecture for knowledge representation of patient information for Covid-19; 2021. http://hdl.handle.net/10204/11827 .TY - Conference Presentation AU - Marais, Laurette AU - Pretorius, L AB - AwezaMed Covid-19 is a multilingual speech-to-speech translation application for screening patients for Covid-19. It enables Englishspeaking health care providers (HCPs) to conduct screenings by asking questions to patients in all other official languages of South-Africa. It uses a multimodal computational grammar translation system to enable English speech and screen-based input, which can be translated to produce synthetic speech in the target languages. Grammatical Framework is used for the translation system, utilising a semantic interlingua. Because of this, each utterance translated by the application is represented by a semantic tree, which could be exploited for knowledge representation. In this paper, we describe how the machine translation architecture designed for multilingual speech-to-speech translation can be adapted for knowledge representation consistent with existing knowledge representation formalisms, namely openEHR archetypes and RDF triples, which could be recorded seamlessly by HCPs during the screening. DA - 2021-02 DB - ResearchSpace DP - CSIR J1 - Proceedings of the 1st South African Conference for Artificial Intelligence Research, (SACAIR 2020), Muldersdrift, South Africa, 22-26 February 2021 KW - Covid-19 KW - Screening KW - openEHR KW - Linked data LK - https://researchspace.csir.co.za PY - 2021 SM - 978-0-620-89373-2 T1 - Exploiting a multilingual semantic Machine translation architecture for knowledge representation of patient information for Covid-19 TI - Exploiting a multilingual semantic Machine translation architecture for knowledge representation of patient information for Covid-19 UR - http://hdl.handle.net/10204/11827 ER -24221