Using structured data to answer consumers health-related questions

Detalhes bibliográficos
Autor(a) principal: Pintor, Hugo Rafael Campinos
Data de Publicação: 2020
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10773/29068
Resumo: The current standard way of searching for information is through the usage of some kind of search engine. Even though there has been progress, it still is mainly based on the retrieval of a list of documents in which the words you searched for appear. Since the users goal is to find an answer to a question, having to look through multiple documents hoping that one of them have the information they are looking for is not very efficient. The aim of this thesis is to improve that process of searching for information, in this case of medical knowledge in two different ways, the first one is replacing the usual keywords used in search engines for something that is more natural to humans, a question in its natural form. The second one is to make use of the additional information that is present in a question format to provide the user an answer for that same question instead of a list of documents where those keywords are present. Since social media are the place where people replace the queries used on a search engine for questions that are usually answered by humans, it seems the natural place to look for the questions that we aim to provide with automatic answers. The first step to provide an answer to those questions will be to classify them in order to find what kind of information should be present in its answer. The second step is to identify the keywords that would be present if this was to be searched through the currently standard way. Having the keywords identified and knowing what kind of information the question aims to retrieve, it is now possible to map it into a query format and retrieve the information needed to provide an answer.
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spelling Using structured data to answer consumers health-related questionsAutomatic question answeringBiomedical question answeringUser generated contentThe current standard way of searching for information is through the usage of some kind of search engine. Even though there has been progress, it still is mainly based on the retrieval of a list of documents in which the words you searched for appear. Since the users goal is to find an answer to a question, having to look through multiple documents hoping that one of them have the information they are looking for is not very efficient. The aim of this thesis is to improve that process of searching for information, in this case of medical knowledge in two different ways, the first one is replacing the usual keywords used in search engines for something that is more natural to humans, a question in its natural form. The second one is to make use of the additional information that is present in a question format to provide the user an answer for that same question instead of a list of documents where those keywords are present. Since social media are the place where people replace the queries used on a search engine for questions that are usually answered by humans, it seems the natural place to look for the questions that we aim to provide with automatic answers. The first step to provide an answer to those questions will be to classify them in order to find what kind of information should be present in its answer. The second step is to identify the keywords that would be present if this was to be searched through the currently standard way. Having the keywords identified and knowing what kind of information the question aims to retrieve, it is now possible to map it into a query format and retrieve the information needed to provide an answer.Atualmente a forma mais comum de procurar informação é através da utilização de um motor de busca. Apesar de haver progresso os seus resultados continuam a ser maioritariamente baseados na devolução de uma lista de documentos onde estão presentes as palavras utilizadas na pesquisa, tendo o utilizador posteriormente que percorrer um conjunto dos documentos apresentados na esperança de obter a informação que procura. Para além de ser uma forma menos natural de procurar informação também é menos eficiente. O objetivo para esta tese é melhorar esse processo de procura de informação, sendo neste caso o foco a área da saúde. Estas melhorias aconteceriam de duas formas diferentes, sendo a primeira a substituição da query normalmente utilizada em motores de busca, por algo que nos é mais natural - uma pergunta. E a segunda seria aproveitar a informação adicional a que temos acesso apenas no formato de pergunta, para fornecer os dados necessários à sua resposta em vez de uma lista de documentos onde um conjunto de palavras-chave estão presentes. Sendo as redes sociais o local onde a busca por informação acontece através da utilização de perguntas, em substituição do que seria normal num motor de busca, pelo facto de a resposta nestas plataformas ser normalmente respondida por humanos e não máquinas. Parece assim ser o local natural para a recolha de perguntas para as quais temos o objetivo de fornecer uma ferramenta para a obtenção automática de uma resposta. O primeiro passo para ser possível fornecer esta resposta será a classificação das perguntas em diferentes tipos, tornando assim possível identificar qual a informação que se pretende obter. O segundo passo será identificar e categorizar as palavras de contexto biomédico presentes no texto fornecido, que seriam aquelas utilizadas caso a procura estivesse a ser feita utilizando as ferramentas convencionais. Tendo as palavras-chave sido identificadas e sabendo qual o tipo de informação que deverá estar presente na sua resposta. É agora possível mapear esta informação para um formato conhecido pelos computadores (query) e assim obter a informação pretendida.2020-08-14T18:36:09Z2020-07-14T00:00:00Z2020-07-14info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10773/29068engPintor, Hugo Rafael Campinosinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-02-22T11:56:14Zoai:ria.ua.pt:10773/29068Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:01:29.720006Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Using structured data to answer consumers health-related questions
title Using structured data to answer consumers health-related questions
spellingShingle Using structured data to answer consumers health-related questions
Pintor, Hugo Rafael Campinos
Automatic question answering
Biomedical question answering
User generated content
title_short Using structured data to answer consumers health-related questions
title_full Using structured data to answer consumers health-related questions
title_fullStr Using structured data to answer consumers health-related questions
title_full_unstemmed Using structured data to answer consumers health-related questions
title_sort Using structured data to answer consumers health-related questions
author Pintor, Hugo Rafael Campinos
author_facet Pintor, Hugo Rafael Campinos
author_role author
dc.contributor.author.fl_str_mv Pintor, Hugo Rafael Campinos
dc.subject.por.fl_str_mv Automatic question answering
Biomedical question answering
User generated content
topic Automatic question answering
Biomedical question answering
User generated content
description The current standard way of searching for information is through the usage of some kind of search engine. Even though there has been progress, it still is mainly based on the retrieval of a list of documents in which the words you searched for appear. Since the users goal is to find an answer to a question, having to look through multiple documents hoping that one of them have the information they are looking for is not very efficient. The aim of this thesis is to improve that process of searching for information, in this case of medical knowledge in two different ways, the first one is replacing the usual keywords used in search engines for something that is more natural to humans, a question in its natural form. The second one is to make use of the additional information that is present in a question format to provide the user an answer for that same question instead of a list of documents where those keywords are present. Since social media are the place where people replace the queries used on a search engine for questions that are usually answered by humans, it seems the natural place to look for the questions that we aim to provide with automatic answers. The first step to provide an answer to those questions will be to classify them in order to find what kind of information should be present in its answer. The second step is to identify the keywords that would be present if this was to be searched through the currently standard way. Having the keywords identified and knowing what kind of information the question aims to retrieve, it is now possible to map it into a query format and retrieve the information needed to provide an answer.
publishDate 2020
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2020-07-14
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