BIGDATA: Small: Big Data for Everyone

大数据：小：每个人的大数据

• 批准号: 1250956
• 负责人: William Cohen
• 金额: $ 54.84万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-01 至 2017-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1250956&HistoricalAwards=false
• 关键词: BIGDATA; Small; Big; Data; Everyone

Although big data has had a huge impact in several areas, this impact is limited by the high cost and poor quality of analyzing unstructured data, and the costs of integrating data of multiple types. Lowering these costs will bring the benefits of big data based research to many new areas. Against this background, this project aims to develop machine-learning methods that read, analyze, and integrate web-scale collections of text and other data. The project can be expected to yield fundamental advances in data integration, machine learning, natural language understanding, and automated inference. The project includes research thrusts in (1) robust semi-supervised bootstrap learning algorithms that can cope with ambiguity in text, (2) algorithms for detecting and aligning the schemas implicit in semi-structured sources relative to a shared common ontology, (3) NLP algorithms that perform deeper analysis on text to extract infrequently mentioned yet important facts, and (4) targeted reading agents capable of pursuing specific queries or conjectures based on the scientist's current focus. Anticipated results of the project include fundamental advances in each of the research thrusts and their synergistic integration into software system (NESSIE) designed to help scientists in exploring scientific hypotheses in their respective domains of interest, by supporting targeted extraction of knowledge from large amounts of textual sources in relevant areas. Broader impacts of the research include advanced techniques for extracting and organizing structured knowledge from text, and integrate the learned information with existing structured knowledge in multiple domains. The Additional broader impacts of the research include enhanced opportunities fore advanced research-based training of graduate students. The softare and data resulting from the research will be made freely available to the larger scientific community.

尽管大数据在多个领域产生了巨大的影响，但这种影响受到分析非结构化数据的高成本和低质量以及集成多种类型数据的成本的限制。降低这些成本将为许多新领域带来基于大数据的研究的好处。在此背景下，该项目旨在开发机器学习方法，以读取、分析和整合网络规模的文本和其他数据集合。该项目有望在数据集成、机器学习、自然语言理解和自动推理方面取得根本性进展。该项目包括以下方面的研究重点：（1）强大的半监督自举学习算法，可以科普文本中的歧义，（2）用于检测和对齐半结构化源中隐含的模式的算法，相对于共享的公共本体，（3）对文本进行更深入分析的NLP算法，以提取不常提到但重要的事实，以及（4）能够基于科学家当前的关注点进行特定查询或查询的目标阅读代理。该项目的预期成果包括每个研究方向的基本进展及其协同集成到软件系统（NESSIE）中，旨在帮助科学家在各自感兴趣的领域探索科学假设，支持从相关领域的大量文本来源中有针对性地提取知识。研究的更广泛的影响包括从文本中提取和组织结构化知识的先进技术，以及将学习到的信息与多个领域的现有结构化知识相结合。该研究的其他更广泛的影响包括增加研究生高级研究培训的机会。研究产生的软件和数据将免费提供给更大的科学界。