SGER: Discovery of Research Trends Using Concept Extraction and Data Mining Techniques in domain-specific Text: Application to Nanoscale Science and Engineering Field.

SGER：在特定领域文本中使用概念提取和数据挖掘技术发现研究趋势：在纳米科学与工程领域的应用。

• 批准号: 0737961
• 负责人: Abdelghani Bellaachia
• 金额: --
• 依托单位: George Washington University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-06-15 至 2008-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0737961&HistoricalAwards=false
• 关键词: SGER; Discovery; Research; Trends; Using

National Science Foundation - Division of Chemical &Transport Systems Particulate & Multiphase Processes Program (1415)Proposal Number: 0737961 Principal Investigator: Bellaachia, A. Affiliation: George Washington University Proposal Title: SGER: Discovery of Research Trends Using Concept Extraction and Data Mining Techniques in domain-specific Text: Application to Nanoscale Science and Engineering Field The purpose of this project is to conduct exploratory research, employing knowledge data discovery techniques that will advance the state-of-the art for extracting, analyzing, understanding, and digesting information about a complex research area from large semi-structured data. When searching for documents that contain the data or topics that one is looking for, the search consists of little more than a keyword matching. In the past this technique has been successful, due to the number of documents that could possibly be returned. However, now that there are trillions of possible documents that fit simple keyword searches, a more sufficient methodology needs to be developed.Concept extraction could be a possible solution to this growing problem. Concept extraction is the process of examining a document programmatically and determining its subject or key ideas. This research will use concept extraction and apply data mining techniques to analyze the online NSF awards with a focus on the nano-scale science and engineering awards. Noun phrases will be extracted from award proposals using existing tools such as General Architecture for Text Engineering (GATE) [10]. The list of noun phrases will be used to describe the content of each award. Two main issues will be addressed in this project (1) the discovery of topics and research trends, and (2) the classification of data according to these topics. The evaluation of our system will be conducted using the online NSF awards and will target the nanoscale scientific and engineering awards.Intellectual MeritThis research addresses problems and opportunities presented by the increasingly complex large semi-structured data available in business, science, and a range of other domains. Current searching techniques do not provide intuitive mechanisms to navigate through different topics in the dataset. The most significant contribution from our previous effort was the establishment of database that stores all nanoscale science and engineering awards with different functionalities. The research objectives of this project are to implement a data mining tool that detects emergent research trends in the area of nanoscale science and engineering fields. This tool will use algorithms that adequate to this type of domain of applications.Broader ImpactThe broader implications of the proposed work are also many as well as significant. Our research can be applied to other domains such bio-informatics. The following broader implications of our research relate to the educational process:- The proposed activity will provide support for graduate and/or undergraduate students.- The findings of this research will be disseminated broadly via conferences and/or journal publications as well as lectures and seminars as opportunities arise.- Finally, the methodology followed in this research will be shared with the students of my data mining class.

国家科学基金会-化学和运输系统颗粒和多阶段工艺计划(1415年)提案编号：0737961首席研究员：贝拉契亚，A.隶属关系：乔治华盛顿大学提案标题：SGER：在特定领域的文本中使用概念提取和数据挖掘技术发现研究趋势：应用于纳米科学和工程领域本项目的目的是进行探索性研究，利用知识数据发现技术，从大型半结构化数据中提取、分析、理解和消化有关复杂研究领域的信息。当搜索包含要查找的数据或主题的文档时，搜索仅由关键字匹配组成。在过去，由于可能返回的文档数量较多，这种技术一直很成功。然而，现在有数以万亿计的文档可能适合简单的关键字搜索，需要开发一种更充分的方法。概念提取可能是解决这个日益增长的问题的可能方案。概念提取是以编程方式检查文档并确定其主题或关键思想的过程。本研究将使用概念抽取和应用数据挖掘技术来分析在线的NSF奖项，重点是纳米级的科学和工程奖项。名词短语将使用现有的工具从获奖提案中提取，例如文本工程的通用架构(GATE)[10]。名词性短语列表将用来描述每个奖项的内容。这个项目将解决两个主要问题(1)主题的发现和研究趋势，以及(2)根据这些主题对数据进行分类。我们系统的评估将使用在线NSF奖进行，并将以纳米级科学和工程奖为目标。智力价值这项研究解决了商业、科学和一系列其他领域中日益复杂的大型半结构化数据带来的问题和机会。当前的搜索技术不提供在数据集中的不同主题中导航的直观机制。我们之前的努力最重要的贡献是建立了数据库，存储了所有具有不同功能的纳米级科学和工程奖。该项目的研究目标是实现一个数据挖掘工具，该工具可以检测纳米科学和工程领域中的新研究趋势。该工具将使用适合这类应用程序领域的算法。布罗德影响拟议工作的更广泛影响也是许多和重要的。我们的研究可以应用于生物信息学等其他领域。我们研究的以下更广泛的影响与教育过程有关：-拟议的活动将为研究生和/或本科生提供支持。-这项研究的结果将通过会议和/或期刊出版物以及有机会的讲座和研讨会广泛传播。-最后，这项研究中遵循的方法将与我的数据挖掘班级的学生分享。