CAREER: From Data to Knowledge: Extracting and Utilizing Concept Graphs in Online Environments

职业：从数据到知识：在线环境中提取和利用概念图

• 批准号: 1652674
• 负责人: Cornelia Caragea
• 金额: $ 49.99万
• 依托单位: University of North Texas
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-06-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1652674&HistoricalAwards=false
• 关键词: CAREER; Data; Knowledge; Extracting; Utilizing

Knowledge bases today are central to the successful utilization of information available in the large and growing amounts of digital data on the Web. Such technologies have started to unleash a transformation of Web search from a keyword match to discovery, learning, and creativity, which are crucial to promoting the goal of knowledge discovery. Unfortunately, the search for information remains inherently difficult for significant portions of the Web such as the Scholarly Web, which contains many millions of scientific documents. For example, PubMed has over 20 million documents, whereas Google Scholar is estimated to have more than 100 million. Open-access digital libraries such as CiteSeerX, which acquire freely-available research articles from the Web, witness an increase in their document collections as well. Despite attractive advancements by scholarly search portals, semantic search technologies that "understand" complex concepts and their relations and can systematically satisfy users' intricate information needs have yet to be investigated on the Scholarly Web. The goal of this project is to design solutions to make information more accessible and comprehensible to Scholarly Web users in particular, and Web users in general, and to help them discover knowledge more effectively and efficiently. The approach taken will be to develop an integrated framework, focusing on the extraction and utilization of scholarly knowledge graphs in online scholarly environments. Educationally, this work will involve: training of graduate, undergraduate, and high-school students, particularly encouraging the participation of women and underrepresented groups in the research efforts; curriculum development and integration of research into courses taught by the PI; exposure of students to industry and international experiences; and education for the general public. The project will target the following research objectives: (1) explore the construction of scholarly knowledge graphs that combine evidence from multiple resources in an open information extraction framework; (2) design and develop novel algorithms for the detection and analysis of interesting and previously unknown connections between concepts, in order to enforce knowledge discovery on the Scholarly Web; and (3) investigate the utility of scholarly knowledge graphs in a question answering system. The results of this research will be integrated into the CiteSeerX digital library (http://citeseerx.ist.psu.edu). The software, tools, and benchmark datasets, which will be developed during the course of this project will be made publicly available. All findings will be shared with the research community through publications in academic journals and presented in Information Retrieval, Text Mining and Natural Language Processing conferences. For further information, see the project web page: http://www.cse.unt.edu/~ccaragea/skg.html.

当今的知识库是成功利用网络上大量数字数据中可用信息的核心。这些技术已经开始释放从关键字匹配到发现，学习和创造力的Web搜索转换，这对于促进知识发现的目标至关重要。不幸的是，对于大部分网络（例如学术网络），对信息的搜索仍然固有地困难，其中包含数百万个科学文档。例如，PubMed拥有超过2000万个文件，而Google Scholar估计拥有超过1亿个文件。从网络中获取可自由获取的研究文章的开放式数字图书馆，例如Citeseerx，也见证了其文档藏品的增加。尽管学术搜索门户网站取得了诱人的进步，但语义搜索技术“了解”复杂的概念及其关系，并且可以系统地满足用户的复杂信息需求，尚未在学术网络上进行调查。该项目的目的是设计解决方案，以使信息更容易访问，尤其是学术网络用户，以及一般的Web用户，并帮助他们更有效，更有效地发现知识。采用的方法是开发一个集成的框架，重点是在线学术环境中学术知识图的提取和利用。在教育上，这项工作将涉及：培训研究生，本科生和高中生，特别是鼓励妇女和人数不足的群体参与研究工作；课程开发和整合在PI教授的课程中；学生接触行业和国际经验；和对公众的教育。该项目将针对以下研究目标：（1）探索在开放信息提取框架中结合了来自多个资源的证据的学术知识图的构建； （2）设计和开发新颖的算法，以检测和分析概念之间有趣且以前未知的联系，以便在学术网络上实施知识发现； （3）在问题回答系统中研究学术知识图的实用性。这项研究的结果将集成到Citeseerx数字图书馆（http://citeseerx.ist.psu.edu）中。将在该项目过程中开发的软件，工具和基准数据集将公开使用。所有发现将通过学术期刊的出版物与研究社区共享，并在信息检索，文本挖掘和自然语言处理会议中介绍。有关更多信息，请参见项目网页：http：//www.cse.unt.edu/~ccaragea/skg.html。