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Register a new userExploration and Discovery of the COVID-19 Literature through Semantic Visualization
استكشاف واكتشاف أدب Covid-19 من خلال التصور الدلالي
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نقترح التصور الدلالي كطريقة تحليلية بصرية لغوية.يمكنها تمكين الاستكشاف والاكتشاف على مجموعات البيانات الكبيرة للشبكات المعقدة من خلال استغلال دلالات العلاقات فيها.ينطوي ذلك على استخراج المعلومات، وتطبيق عمليات الحد من المعلمات، وبناء تمثيل البيانات الهرمية وتصميم التصور.نقدم أيضا نظام التصور القابل للبحث والتفاعل في Covid-Semviz المرافق للاستكشاف عن بيانات Covid-19 لإظهار تطبيق طريقةنا المقترحة.في دراسات المستخدمين، وجد المستخدمون أن CAVID-Semviz المدعوم بالتصور الدقيق مفيدة من حيث إيجاد المعلومات ذات الصلة واكتشاف جمعيات غير معروفة.
We propose semantic visualization as a linguistic visual analytic method. It can enable exploration and discovery over large datasets of complex networks by exploiting the semantics of the relations in them. This involves extracting information, applying parameter reduction operations, building hierarchical data representation and designing visualization. We also present the accompanying COVID-SemViz a searchable and interactive visualization system for knowledge exploration of COVID-19 data to demonstrate the application of our proposed method. In the user studies, users found that semantic visualization-powered COVID-SemViz is helpful in terms of finding relevant information and discovering unknown associations.
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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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The COVID-19 pandemic has spawned a diverse body of scientific literature that is challenging to navigate, stimulating interest in automated tools to help find useful knowledge. We pursue the construction of a knowledge base (KB) of mechanisms---a fu
ndamental concept across the sciences, which encompasses activities, functions and causal relations, ranging from cellular processes to economic impacts. We extract this information from the natural language of scientific papers by developing a broad, unified schema that strikes a balance between relevance and breadth. We annotate a dataset of mechanisms with our schema and train a model to extract mechanism relations from papers. Our experiments demonstrate the utility of our KB in supporting interdisciplinary scientific search over COVID-19 literature, outperforming the prominent PubMed search in a study with clinical experts. Our search engine, dataset and code are publicly available.
In this paper, we present ArCOV-19, an Arabic COVID-19 Twitter dataset that spans one year, covering the period from 27th of January 2020 till 31st of January 2021. ArCOV-19 is the first publicly-available Arabic Twitter dataset covering COVID-19 pan
demic that includes about 2.7M tweets alongside the propagation networks of the most-popular subset of them (i.e., most-retweeted and -liked). The propagation networks include both retweetsand conversational threads (i.e., threads of replies). ArCOV-19 is designed to enable research under several domains including natural language processing, information retrieval, and social computing. Preliminary analysis shows that ArCOV-19 captures rising discussions associated with the first reported cases of the disease as they appeared in the Arab world.In addition to the source tweets and the propagation networks, we also release the search queries and the language-independent crawler used to collect the tweets to encourage the curation of similar datasets.
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