{"id":2861,"date":"2020-09-10T22:12:12","date_gmt":"2020-09-11T01:12:12","guid":{"rendered":"http:\/\/sbbd.org.br\/2020\/short-course-2-copy\/"},"modified":"2021-04-27T15:34:08","modified_gmt":"2021-04-27T18:34:08","slug":"short-course-1","status":"publish","type":"page","link":"https:\/\/sbbd.org.br\/2020\/short-course-1\/","title":{"rendered":"Short Course #1"},"content":{"rendered":"\n<p><strong>Watch the complete <a href=\"https:\/\/www.youtube.com\/watch?v=hWaoEOdTwas&amp;list=PLRKeuVfLlY-5IZme8klDjd0S7I6QWUPQv&amp;index=1\"><span class=\"has-inline-color has-vivid-cyan-blue-color\">video<\/span><\/a> presentation.<\/strong><\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Daniel Ramos da Silva, Artur Ziviani e Fabio Porto<\/h4>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\"><p><strong><span style=\"color:#150996\" class=\"has-inline-color\"><strong>Aprendizado de M\u00e1quina aplicado a Grafos de Conhecimento<\/strong><\/span><\/strong><\/p><\/blockquote>\n\n\n\n<div class=\"wp-block-file\"><a href=\"https:\/\/sbbd.org.br\/2020\/wp-content\/uploads\/sites\/13\/2020\/10\/Aprendizado-de-Maquina-aplicado-a-Grafos-de-Conhecimento-3-Unicode-Encoding-Conflict.pdf\">PDF<\/a><a href=\"https:\/\/sbbd.org.br\/2020\/wp-content\/uploads\/sites\/13\/2020\/10\/Aprendizado-de-Maquina-aplicado-a-Grafos-de-Conhecimento-3-Unicode-Encoding-Conflict.pdf\" class=\"wp-block-file__button\" download>Download<\/a><\/div>\n\n\n\n<p><strong>Abstract<\/strong><\/p>\n\n\n\n<p>The increasing production and availability of massive and heterogeneous data bring forward challenging opportunities. Among them, the development of computing systems capable of learning, reasoning, and inferring facts based on prior knowledge is an important task. In this scenario, knowledge bases are valuable assets for the knowledge representation and automated reasoning of diverse application domains. Especially, inference tasks on knowledge graphs (knowledge bases\u2019 graphical representations) are increasingly important in academia and industry. In this short course, we introduce machine learning methods and techniques employed in knowledge graph inference tasks as well as discuss the technical and scientific challenges and opportunities associated with those tasks.<\/p>\n\n\n\n<p><strong>Resumo<\/strong><\/p>\n\n\n\n<p>Com o an\u00fancio do Google Knowledge Graph em 2012, o interesse de academia e ind\u00fastria se voltou de maneira significativa para grafos de conhecimento. Desde ent\u00e3o, essa classe de grafos tem se tornado t\u00f3pico recorrente em diversos cen\u00e1rios de aplica\u00e7\u00e3o, muito em virtude de seu potencial tanto na integra\u00e7\u00e3o de cole\u00e7\u00f5es de dados heterog\u00eaneas e de larga escala, quanto na representa\u00e7\u00e3o de conhecimento para sistemas inteligentes. Contudo, desafios tecnol\u00f3gicos e cient\u00edficos s\u00e3o intr\u00ednsecos a tarefas relacionadas a grafos de conhecimento, como sua constru\u00e7\u00e3o e infer\u00eancia. Em especial, cada vez mais, esses desafios s\u00e3o enfrentados por meio abordagens baseadas em Aprendizado de M\u00e1quina. Nesse contexto, este minicurso apresenta uma introdu\u00e7\u00e3o aos m\u00e9todos e t\u00e9cnicas de Aprendizado de M\u00e1quina empregadas atualmente em tarefas associadas a grafos de conhecimento, assim como, discute suas oportunidades de pesquisa e desenvolvimento.<\/p>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<p> <\/p>\n","protected":false},"excerpt":{"rendered":"<p>Watch the complete video presentation. Daniel Ramos da Silva, Artur Ziviani e Fabio Porto Aprendizado de M\u00e1quina aplicado a Grafos&hellip; <\/p>\n","protected":false},"author":13,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"templates\/template-fullwidth.php","meta":{"footnotes":""},"class_list":["post-2861","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/sbbd.org.br\/2020\/wp-json\/wp\/v2\/pages\/2861","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sbbd.org.br\/2020\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sbbd.org.br\/2020\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sbbd.org.br\/2020\/wp-json\/wp\/v2\/users\/13"}],"replies":[{"embeddable":true,"href":"https:\/\/sbbd.org.br\/2020\/wp-json\/wp\/v2\/comments?post=2861"}],"version-history":[{"count":11,"href":"https:\/\/sbbd.org.br\/2020\/wp-json\/wp\/v2\/pages\/2861\/revisions"}],"predecessor-version":[{"id":3564,"href":"https:\/\/sbbd.org.br\/2020\/wp-json\/wp\/v2\/pages\/2861\/revisions\/3564"}],"wp:attachment":[{"href":"https:\/\/sbbd.org.br\/2020\/wp-json\/wp\/v2\/media?parent=2861"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}