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: 1802358
• 负责人: Cornelia Caragea
• 金额: $ 49.99万
• 依托单位: Kansas State University
• 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: 2017-09-13 至 2019-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1802358&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.

如今，知识库对于成功利用网络上大量且不断增长的数字数据中的可用信息至关重要。这些技术已经开始推动网络搜索从关键词匹配向发现、学习和创造力的转变，这对于促进知识发现的目标至关重要。不幸的是，对于网络的重要部分（例如包含数百万科学文档的学术网络）来说，搜索信息本质上仍然很困难。例如，PubMed 拥有超过 2000 万份文档，而 Google Scholar 估计拥有超过 1 亿份文档。诸如 CiteSeerX 之类的开放获取数字图书馆可以从网络上免费获取研究文章，其文献收藏也有所增加。尽管学术搜索门户取得了引人注目的进步，但“理解”复杂概念及其关系并能够系统地满足用户复杂信息需求的语义搜索技术尚未在学术网络上得到研究。该项目的目标是设计解决方案，使信息对于学术 Web 用户（尤其是一般 Web 用户）更容易访问和理解，并帮助他们更有效地发现知识。采取的方法将是开发一个综合框架，重点关注在线学术环境中学术知识图的提取和利用。在教育方面，这项工作将涉及： 培训研究生、本科生和高中生，特别是鼓励妇女和代表性不足的群体参与研究工作；课程开发以及将研究整合到 PI 教授的课程中；让学生接触行业和国际经验；和对公众的教育。该项目将实现以下研究目标：（1）探索学术知识图谱的构建，在开放的信息提取框架中结合多种资源的证据； (2) 设计和开发新颖的算法，用于检测和分析概念之间有趣的和以前未知的联系，以加强学术网络上的知识发现； (3) 研究学术知识图在问答系统中的效用。这项研究的结果将被整合到 CiteSeerX 数字图书馆 (http://citeseerx.ist.psu.edu) 中。在该项目过程中开发的软件、工具和基准数据集将公开提供。所有研究结果将通过学术期刊上的出版物与研究界分享，并在信息检索、文本挖掘和自然语言处理会议上展示。有关更多信息，请参阅项目网页：http://www.cse.unt.edu/~ccaragea/skg.html。