III: Small: Integrated Digital Event Archiving and Library (IDEAL)

III：小型：集成数字事件归档和图书馆 (IDEAL)

• 批准号: 1319578
• 负责人: Edward Fox
• 金额: $ 50万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1319578&HistoricalAwards=false
• 关键词: III; Small; Integrated; Digital; Event

The Integrated Digital Event Archive and Library (IDEAL) system addresses the need for combining the best of digital library and archive technologies in support of stakeholders who are remembering and/or studying important events. It extends the work at Virginia Tech on the Crisis, Tragedy, and Recovery network (see http://www.ctrnet.net) to handle government and community events, in addition to a range of significant natural or manmade disasters. It addresses needs of those interested in emergency preparedness/response, digital government, and the social sciences. It proves the effectiveness of the 5S (Societies, Scenarios, Spaces, Structures, Streams) approach to intelligent information systems by crawling and archiving events of broad interest. It leverages and extends the capabilities of the Internet Archive to develop spontaneous event collections that can be permanently archived as well as searched and accessed, and of the LucidWorks Big Data software that supports scalable indexing, analyzing, and accessing of very large collections. Through a new model-based approach to intelligent focused crawling, it improves the quality (e.g., accuracy, coverage, and elimination of noise) of collections of webpages so as to ensure comprehensiveness, balance, and low bias, as is needed for scholarly study of historically important events by social scientists. It incorporates a range of visualization capabilities in support of key stakeholder communities, including archivists, librarians, researchers, scholars, and the general public. IDEAL connects the processing of tweets and webpages, combining informal and formal media, to automatically detect important events, as well as to support building collections on chosen general or specific topics. It supports integration of multiple types and at multiple levels, including key models about the event it is crawling (event models), the sources of information about the event (source models), the mechanisms used for disseminating information about the event (publishing venue models), and the entities related to the event (society /organization models). Integrated services include topic identification, categorization (building upon special ontologies being devised), sentiment analysis, and visualization of data, information, and context.The IDEAL website (http://www.eventsarchive.org) supports searching, browsing, analyzing, and visualizing of event collections (of both tweets and webpages), as well as access to project software, methods, findings, publications, and other results. Usage is encouraged of the integrated system along with a growing number of collections, as well as of particular tools such as for focused crawling, which should aid curators to avoid non-relevant content while including a broader range of sources, improving significantly upon current crawling and archiving methods. Important data and information on events of interest are saved rather than lost, helping preserve our history and culture, in support of public interest, education, policy making, historical analyses, and comparative studies. Students studying sociology, human-computer interaction, digital libraries, information retrieval, computational linguistics, multimedia, and hypertext are gaining experience and contributing in scholarly studies, algorithms, software, interfaces, and big data handling.

集成的数字事件档案和图书馆（理想）系统解决了结合数字图书馆和档案技术的最佳需求，以支持记住和/或研究重要事件的利益相关者。它扩展了弗吉尼亚理工大学在危机，悲剧和恢复网络方面的工作（请参阅http://www.ctrnet.net）来处理政府和社区活动，此外还有一系列重大的自然或人造灾难。它解决了对紧急准备/响应，数字政府和社会科学感兴趣的人的需求。它通过爬行和归档广泛关注的事件来证明5S（社会，场景，空间，结构，流）方法的有效性。它利用并扩展了Internet档案的功能，以开发可以永久存档以及搜索和访问的自发活动集合，以及支持可扩展索引，分析和访问非常大的集合的Lucidworks大数据软件。通过一种基于模型的智能专注爬行方法，它可以提高网页集合集合的质量（例如准确性，覆盖和消除噪音），以确保社会科学家对历史上重要事件的学术研究所需的全面性，平衡和低偏见。它结合了一系列可视化功能，以支持关键的利益相关者社区，包括档案管理员，图书馆员，研究人员，学者和公众。理想连接了推文和网页的处理，将非正式和正式媒体结合起来，以自动检测重要事件，并支持对所选一般或特定主题的建筑集合。它支持多种类型和多个级别的集成，包括有关其爬网事件（事件模型），有关事件的信息的源头（源模型），用于传播有关事件（发布场地模型）信息的机制以及与事件（事件 /组织 /组织模型）相关的实体的机制。综合服务包括主题识别，分类（基于设计的特殊本体论），情感分析和可视化数据，信息和上下文。理想的网站（http://www.eventsarchive.org）支持搜索，浏览，分析和可视化事件集合（Tweets和网页）的访问和其他软件，以及其他软件，以及其他软件，以及其他方法，以及其他方法，以及其他方法，以及其他方法，以及其他方法，以及其他方法，以及解决方案。鼓励使用集成系统以及越来越多的收藏品以及特定工具（例如集中爬行）的用法，这应该有助于策展人避免使用非相关内容，同时包括更广泛的来源，从而在当前的爬行和归档方法上显着改善。为了支持公共利益，教育，政策制定，历史分析和比较研究，保存了有关感兴趣事件的重要数据和信息。研究社会学，人力计算，数字图书馆，信息检索，计算语言学，多媒体和超文本的学生在学术研究，算法，软件，界面和大数据处理方面都获得了经验和贡献。

项目成果

Twitter-Based Knowledge Graph for Researchers

为研究人员提供基于 Twitter 的知识图谱

• 发表时间: 2020
• 期刊: Virginia tech
• 作者: Vincent, Kyle;Meno, Emma
• 通讯作者: Meno, Emma

Analysis of Moving Events Using Tweets

使用推文分析移动事件

• 发表时间: 2019
• 期刊: Virginia tech
• 作者: Patil, Supritha Basavaraj

Teaching Natural Language Processing through Big Data Text Summarization with Problem-Based Learning

通过大数据文本摘要和基于问题的学习来教授自然语言处理

• DOI: 10.2478/dim-2020-0003
• 发表时间: 2020
• 期刊: Data and Information Management
• 作者: Li, Liuqing;Geissinger, Jack;Ingram, William A.;Fox, Edward A.
• 通讯作者: Fox, Edward A.