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Register a new userCOVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation
Covid-19 الأدب المعرفة الرسم البياني البناء والتدقيق
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لمكافحة Covid-19، يحتاج كلا من الأطباء والعلماء إلى هضم كمية شاسعة من المعرفة الطبية الحيوية ذات الصلة في الأدب لفهم آلية المرض والوظائف البيولوجية ذات الصلة.لقد قمنا بتطوير إطار اكتشاف رواية وشامل للمعرفة، Covid-KG لاستخراج عناصر المعرفة بالوسائط المتعددة المحتلة الجميلة (الكيانات والعلاقات والأحداث) من الأدبيات العلمية.ثم نستغل الرسوم البيانية المعرفة بالوسائط المتعددة المبنية (KGS) على الإجابة على السؤال وتوليد التقارير، باستخدام المخدرات تسديدها كدراسة حالة.يوفر إطار عملنا أيضا جمل سياقية مفصلة، فرعية فرعية، وبرقراطية المعرفة كدليل.جميع البيانات، KGS، تقارير.
To combat COVID-19, both clinicians and scientists need to digest the vast amount of relevant biomedical knowledge in literature to understand the disease mechanism and the related biological functions. We have developed a novel and comprehensive knowledge discovery framework, COVID-KG to extract fine-grained multimedia knowledge elements (entities, relations and events) from scientific literature. We then exploit the constructed multimedia knowledge graphs (KGs) for question answering and report generation, using drug repurposing as a case study. Our framework also provides detailed contextual sentences, subfigures, and knowledge subgraphs as evidence. All of the data, KGs, reports.
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This paper presents the preliminary results of an ongoing project that analyzes the growing body of scientific research published around the COVID-19 pandemic. In this research, a general-purpose semantic model is used to double annotate a batch of 5
00 sentences that were manually selected from the CORD-19 corpus. Afterwards, a baseline text-mining pipeline is designed and evaluated via a large batch of 100,959 sentences. We present a qualitative analysis of the most interesting facts automatically extracted and highlight possible future lines of development. The preliminary results show that general-purpose semantic models are a useful tool for discovering fine-grained knowledge in large corpora of scientific documents.
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Relations in most of the traditional knowledge graphs (KGs) only reflect static and factual connections, but fail to represent the dynamic activities and state changes about entities. In this paper, we emphasize the importance of incorporating events
in KG representation learning, and propose an event-enhanced KG embedding model EventKE. Specifically, given the original KG, we first incorporate event nodes by building a heterogeneous network, where entity nodes and event nodes are distributed on the two sides of the network inter-connected by event argument links. We then use entity-entity relations from the original KG and event-event temporal links to inner-connect entity and event nodes respectively. We design a novel and effective attention-based message passing method, which is conducted on entity-entity, event-entity, and event-event relations to fuse the event information into KG embeddings. Experimental results on real-world datasets demonstrate that events can greatly improve the quality of the KG embeddings on multiple downstream tasks.
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