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

今天的知识库是成功利用Web上大量和不断增长的数字数据中可用信息的核心。这些技术已经开始引发网络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Web的重要部分，如包含数百万科学文档的学术Web，搜索信息仍然是固有的困难。例如，PubMed有超过2000万份文档，而谷歌学者估计有超过1亿份。开放访问的数字图书馆，如CiteSeerX，从网络上免费获取研究文章，他们的文献收藏也在增加。尽管学术搜索门户网站取得了令人瞩目的进步，但语义搜索技术还没有在学术网络上得到研究，这些技术可以“理解”复杂的概念及其关系，并能够系统地满足用户复杂的信息需求。这个项目的目标是设计解决方案，使信息更容易为学术Web用户，以及一般Web用户所访问和理解，并帮助他们更有效和高效地发现知识。所采取的办法将是制定一个综合框架，重点是在线学术环境中学术知识图谱的提取和利用。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；制定课程并将研究纳入国际和平研究所教授的课程；让学生接触工业和国际经验；以及对普通公众进行教育。该项目将针对以下研究目标：(1)探索在开放的信息提取框架中结合来自多个资源的证据的学术知识图的构建；(2)设计和开发新的算法来检测和分析概念之间有趣的和以前未知的联系，以便在学术Web上执行知识发现；以及(3)调查学术知识图在问答系统中的实用性。本研究成果将整合到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向公众开放。所有研究成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公布。有关更多信息，请参阅项目网页：http://www.cse.unt.edu/~ccaragea/skg.html.

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.