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dc.contributor.authorYu, Hong Qingen
dc.date.accessioned2019-10-04T09:40:07Z
dc.date.available2019-10-04T09:40:07Z
dc.date.issued2019-08-21
dc.identifier.citationYu H. (2019) 'Mining symptom and disease web data with NLP and Open Linked Data', The 5th World Congress on Electrical Engineering and Computer Systems and Sciences - Lisbon, Avestia Publishing.en
dc.identifier.doi10.11159/mvml19.108
dc.identifier.urihttp://hdl.handle.net/10547/623512
dc.description.abstract- Machine Learning (ML) technologies in recent years are widely applied in various areas to assist knowledge gaining and decision-making on healthcare. However, there is no reliable dataset that contains semantic structured knowledge on symptom and disease enable to apply advanced machine learning algorithms such clustering or prediction. In this paper, we propose a framework that can extract data from web with apply Natural Language Processing (NLP) process and semantic annotation to create Open Linked Data (OLD) bas
dc.language.isoenen
dc.publisherAvestia Publishingen
dc.relation.urlhttp://avestia.com/EECSS2019_Proceedings/files/paper/MVML/MVML_108.pdfen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectmachine learningen
dc.subjectsemantic weben
dc.subjectnatural language processingen
dc.subjectLinked Dataen
dc.subjectG560 Data Managementen
dc.titleMining symptom and disease web data with NLP and Open Linked Dataen
dc.typeConference papers, meetings and proceedingsen
dc.contributor.departmentUniversity of Bedfordshireen
dc.date.updated2019-10-04T09:35:17Z
dc.description.noteOpen access paper https://eecss.org/submissions: "Copyright and Access: The proceedings and related author-guidelines are all based on the open-access model, which means interested individuals and institutions can access the material for free. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles in this proceedings without asking prior permission from the publisher or the author. This is in accordance with the BOAI definition of open access."
html.description.abstract- Machine Learning (ML) technologies in recent years are widely applied in various areas to assist knowledge gaining and decision-making on healthcare. However, there is no reliable dataset that contains semantic structured knowledge on symptom and disease enable to apply advanced machine learning algorithms such clustering or prediction. In this paper, we propose a framework that can extract data from web with apply Natural Language Processing (NLP) process and semantic annotation to create Open Linked Data (OLD) bas


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