III: Small: Collaborative Research: Global Event and Trend Archive Research (GETAR)

III：小型：协作研究：全球事件和趋势档案研究 (GETAR)

• 批准号: 1619028
• 负责人: Edward Fox
• 金额: $ 44.6万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1619028&HistoricalAwards=false
• 关键词: III; Small; Collaborative; Research; Global

This project will devise interactive, integrated, digital library/archive systems coupled with linked and expert-curated webpage/tweet collections, covering key parts of the 1997-2020 timeframe, supporting research on urgent global challenge events and initiatives. It will allow diverse stakeholder communities to interactively collect, organize, browse, visualize, study, analyze, summarize, and explore content and sources related to biodiversity, climate change, crises, disasters, elections, energy policy, environmental policy/planning, geospatial information, green engineering, human rights, inequality, migrations, nuclear power, population growth, resiliency, shootings, sustainability, violence, etc. Studying and addressing important global issues, by scholars, the public, and K-12 students, will be enhanced through tailored interfaces coupled with important collections that will be primary resources for understanding the modern world and its challenges, as well as initiatives, trends, and solutions.Research will extend work on modeling trends, events, and sources, to guide focused crawling, information extraction, tagging, and collaboration. Domain experts will leverage rich event models, exploiting the generality of the 5S framework (Societies, Scenarios, Spaces, Structure, Streams), extending from word, n-gram, topic, concept, and language models. This research will enable efficient assembly of knowledge bases, rapid prototyping of interfaces, gathering/curation of collections with high precision and recall, and flexible discovery in support of research and learning. Interdisciplinary research advances will address digital humanities, web archiving, information retrieval, natural language processing, machine learning, and the construction of valuable interactive/collaborative interfaces. For further information see the project web site at eventsarchive.org.

该项目将设计交互式，集成的数字图书馆/档案系统，再加上链接和专家策划的网页/推文馆藏，涵盖1997 - 2020年时间表的关键部分，支持有关紧急全球挑战事件和计划的研究。 It will allow diverse stakeholder communities to interactively collect, organize, browse, visualize, study, analyze, summarize, and explore content and sources related to biodiversity, climate change, crises, disasters, elections, energy policy, environmental policy/planning, geospatial information, green engineering, human rights, inequality, migrations, nuclear power, population growth, resiliency, shootings, sustainability, violence, etc. Studying and addressing important学者，公众和K-12学生的全球问题将通过量身定制的界面以及重要的收藏来增强，这些界面将成为理解现代世界及其挑战的主要资源，以及计划，趋势和解决方案。研究将扩展趋势，活动和来源的工作，以指导集中的循环，以指导集中的循环浏览，信息提取，信息提取，标签，标签，标签，和合作，并进行了合作，和合作。领域专家将利用丰富的事件模型，利用5S框架（社会，场景，空间，结构，流）的一般性，从单词，n-gram，主题，概念和语言模型延伸。这项研究将使知识库有效地组装，界面的快速原型制作，以高精度和回忆的收集收集/策划以及支持研究和学习的灵活发现。跨学科研究的进步将解决数字人文，网络档案，信息检索，自然语言处理，机器学习以及构建有价值的交互式/协作界面。有关更多信息，请参见expertarchive.org的项目网站。

项目成果

Adversarial Factorization Autoencoder for Look-alike Modeling

• DOI: 10.1145/3357384.3357807
• 发表时间: 2019-11
• 期刊: Proceedings of the 28th ACM International Conference on Information and Knowledge Management
• 作者: Khoa D. Doan;Pranjul Yadav;Chandan K. Reddy

A Sparse Topic Model for Extracting Aspect-Specific Summaries from Online Reviews

• DOI: 10.1145/3178876.3186069
• 发表时间: 2018-04
• 期刊: Proceedings of the 2018 World Wide Web Conference
• 作者: Vineeth Rakesh;Weicong Ding;Aman Ahuja;Nikhil S. Rao;Yifan Sun;Chandan K. Reddy

Pre-Processing Censored Survival Data Using Inverse Covariance Matrix Based Calibration

使用基于逆协方差矩阵的校准来预处理删失生存数据

• DOI: 10.1109/tkde.2017.2719028
• 发表时间: 2017
• 期刊: IEEE Transactions on Knowledge and Data Engineering
• 影响因子: 8.9
• 作者: Vinzamuri, Bhanukiran;Li, Yan;Reddy, Chandan K.
• 通讯作者: Reddy, Chandan K.

Predicting interval time for reciprocal link creation using survival analysis

• DOI: 10.1007/s13278-018-0494-1
• 发表时间: 2018-03
• 期刊: Social Network Analysis and Mining
• 影响因子: 2.8
• 作者: Vachik S. Dave;M. Hasan;Baichuan Zhang;Chandan K. Reddy

LATTE: Latent Type Modeling for Biomedical Entity Linking

• DOI: 10.1609/aaai.v34i05.6526
• 发表时间: 2019-11
• 期刊: ArXiv
• 作者: Ming Zhu;B. Celikkaya;Parminder Bhatia;Chandan K. Reddy