{"id":151,"date":"2022-07-22T22:05:48","date_gmt":"2022-07-22T21:05:48","guid":{"rendered":"http:\/\/en.jingcs.ml\/?p=151"},"modified":"2022-07-24T22:55:17","modified_gmt":"2022-07-24T21:55:17","slug":"ei-an-efficient-tool-for-semantic-biomedical-document-analysis","status":"publish","type":"post","link":"https:\/\/jingcs.com\/index.php\/ei-an-efficient-tool-for-semantic-biomedical-document-analysis\/","title":{"rendered":"[EI] An Efficient Tool for Semantic Biomedical Document Analysis"},"content":{"rendered":"<p>Previous research experience.<br \/>\nOnline link: <a href=\"https:\/\/link.springer.com\/chapter\/10.1007\/978-981-33-6757-9_63\">Link<\/a><\/p>\n<h2>Abstract<\/h2>\n<p>Semantic text mining is a challenging research topic in recent years. Many types of research focus on measuring the similarity of two documents with ontologies such as Medical Subject Headings (Mesh) and Gene Ontology (GO). However, most of the researches considered the single relationship in an ontology. To represent the document comprehensively, a semantic document similarity calculation method is proposed, based on utilizing Average Maximum Match algorithm with double-relations in GO. In the experiment, the results show that the double-relations based similarity calculation method is better than traditional semantic similarity measurements.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Previous research experience. Online link: Link Abstract Semantic text mining is a challenging research topic in recent years. Many types of research focus on measuring the similarity of two documents with ontologies such as Medical Subject Headings (Mesh) and Gene Ontology (GO). However, most of the researches considered the single relationship in an ontology. To<\/p><\/div>\n<div class=\"blog-btn\"><a href=\"https:\/\/jingcs.com\/index.php\/ei-an-efficient-tool-for-semantic-biomedical-document-analysis\/\" class=\"home-blog-btn\">Read More<\/a><\/p>\n","protected":false},"author":1,"featured_media":153,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[34,28],"tags":[51,53,52,49,50],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v19.10 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>[EI] An Efficient Tool for Semantic Biomedical Document Analysis - Jingyu\u2018s Blog<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/jingcs.com\/index.php\/ei-an-efficient-tool-for-semantic-biomedical-document-analysis\/\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"[EI] An Efficient Tool for Semantic Biomedical Document Analysis - Jingyu\u2018s Blog\" \/>\n<meta property=\"og:description\" content=\"Previous research experience. Online link: Link Abstract Semantic text mining is a challenging research topic in recent years. Many types of research focus on measuring the similarity of two documents with ontologies such as Medical Subject Headings (Mesh) and Gene Ontology (GO). However, most of the researches considered the single relationship in an ontology. ToRead More\" \/>\n<meta property=\"og:url\" content=\"https:\/\/jingcs.com\/index.php\/ei-an-efficient-tool-for-semantic-biomedical-document-analysis\/\" \/>\n<meta property=\"og:site_name\" content=\"Jingyu\u2018s Blog\" \/>\n<meta property=\"article:published_time\" content=\"2022-07-22T21:05:48+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2022-07-24T21:55:17+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/jingcs.com\/wp-content\/uploads\/2022\/07\/paper2020.png\" \/>\n\t<meta property=\"og:image:width\" content=\"2000\" \/>\n\t<meta property=\"og:image:height\" content=\"930\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"jingyu\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"jingyu\" \/>\n\t<meta name=\"twitter:label2\" content=\"Estimated reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/jingcs.com\/index.php\/ei-an-efficient-tool-for-semantic-biomedical-document-analysis\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/jingcs.com\/index.php\/ei-an-efficient-tool-for-semantic-biomedical-document-analysis\/\"},\"author\":{\"name\":\"jingyu\",\"@id\":\"https:\/\/jingcs.com\/#\/schema\/person\/c073eb7c1955a530dece0ad363304379\"},\"headline\":\"[EI] An Efficient Tool for Semantic Biomedical Document Analysis\",\"datePublished\":\"2022-07-22T21:05:48+00:00\",\"dateModified\":\"2022-07-24T21:55:17+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/jingcs.com\/index.php\/ei-an-efficient-tool-for-semantic-biomedical-document-analysis\/\"},\"wordCount\":107,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/jingcs.com\/#\/schema\/person\/c073eb7c1955a530dece0ad363304379\"},\"keywords\":[\"Clustering\",\"LayUI\",\"Neo4j\",\"Ontology\",\"Paper\"],\"articleSection\":[\"Programming\",\"Projects\"],\"inLanguage\":\"en-GB\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/jingcs.com\/index.php\/ei-an-efficient-tool-for-semantic-biomedical-document-analysis\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/jingcs.com\/index.php\/ei-an-efficient-tool-for-semantic-biomedical-document-analysis\/\",\"url\":\"https:\/\/jingcs.com\/index.php\/ei-an-efficient-tool-for-semantic-biomedical-document-analysis\/\",\"name\":\"[EI] An Efficient Tool for Semantic Biomedical Document Analysis - 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