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

美国国家科学基金会-化学与运输系统分部-颗粒与多相过程项目（1415）提案编号：0737961首席研究员：Bellaachia， A.隶属机构：乔治华盛顿大学提案标题：SGER：在特定领域使用概念提取和数据挖掘技术发现研究趋势该项目的目的是开展探索性研究，采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。在搜索包含所查找的数据或主题的文档时，搜索只包含一个关键字匹配。在过去，由于可能返回的文档的数量，这种技术是成功的。然而，现在有数万亿可能的文档适合简单的关键字搜索，因此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严重的问题的一种可能的方法。概念提取是通过编程检查文档并确定其主题或关键思想的过程。本研究将采用概念提取和数据挖掘技术对NSF在线奖项进行分析，重点关注纳米尺度的科学和工程奖项。名词短语将使用现有的工具，如文本工程通用架构（GATE）[10]，从获奖提案中提取。名词短语列表将用于描述每个奖项的内容。本项目将解决两个主要问题(1)发现主题和研究趋势，以及(2)根据这些主题对数据进行分类。我们的系统的评估将使用在线NSF奖进行，并将针对纳米级科学和工程奖。本研究解决了商业、科学和一系列其他领域中日益复杂的大型半结构化数据所带来的问题和机遇。当前的搜索技术没有提供直观的机制来导航数据集中的不同主题。我们之前的工作最重要的贡献是建立了一个数据库，该数据库存储了所有具有不同功能的纳米级科学和工程奖项。该项目的研究目标是实现一种数据挖掘工具，用于检测纳米尺度科学和工程领域的新兴研究趋势。该工具将使用适合此类应用程序领域的算法。更广泛的影响拟议工作的更广泛的影响也很多，而且很重要。我们的研究可以应用到其他领域，如生物信息学。我们的研究对教育过程有以下更广泛的影响：-拟议的活动将为研究生和/或本科生提供支持。-这项研究的结果将在有机会时通过会议和/或期刊出版物以及讲座和研讨会广泛传播。-最后，本研究所采用的方法将与我的数据挖掘课的学生分享。

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.