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.

今天，知识库是成功利用网络上大量和不断增长的数字数据中的信息的核心。这些技术已经开始释放Web搜索从关键字匹配到发现，学习和创造力的转变，这对促进知识发现的目标至关重要。不幸的是，对于包含数百万科学文献的学术网等网络的重要部分来说，搜索信息仍然是固有的困难。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。像CiteSeerX这样的开放式数字图书馆从网络上免费获取研究文章，其文献收藏也有所增加。尽管学术搜索门户网站取得了引人注目的进步，语义搜索技术，“理解”复杂的概念和它们的关系，并能系统地满足用户的复杂的信息需求，尚未被调查的学术网站。该项目的目标是设计解决方案，使信息更容易获得和理解的学术网站用户，特别是一般的网络用户，并帮助他们更有效地发现知识。采取的方法将是制定一个综合框架，侧重于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;课程编制和将研究纳入方案研究所教授的课程;使学生接触工业和国际经验;以及对公众进行教育。该项目将针对以下研究目标：（1）探索构建学术知识图谱，该图谱将来自多个资源的联合收割机证据结合在一个开放的信息提取框架中;（2）设计和开发新的算法，用于检测和分析概念之间有趣和以前未知的联系，以加强学术网络上的知识发现;以及（3）研究学术知识图在问答系统中的应用。这项研究的结果将纳入CiteSeerX数字图书馆（http：citeseerx.ist.psu.edu）。将在本项目期间开发的软件、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发表。欲了解更多信息，请参阅项目网页：http://www.cse.unt.edu/~ccaragea/skg.html。