TLS: Assessing and Predicting Scientific Progress through Computational Language Understanding

TLS：通过计算语言理解评估和预测科学进步

• 批准号: 0915730
• 负责人: James Evans
• 金额: $ 39.57万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-15 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0915730&HistoricalAwards=false
• 关键词: TLS; Assessing; Predicting; Scientific; Progress

This project provides new approaches to the evaluation of scientific and technological promise. The basic insight is that scientific concepts, like organisms within ecologies, only exist in networks of supporting ideas, and this is key to understanding the way in which scientific concepts are adopted and diffused. The particular discipline that is studied is chemistry and related disciplines.The work has three stages. First, it assesses and predicts innovation in science from the novelty and popularity of terms and statements within a scientific network. Second, it assesses and predicts the integration of scientific knowledge from term and statement linkage, repetition, and elaboration patterns. Finally, it assesses and predicts success along the path from science to technology by linking term and statement connections to problems. The research uses cybertools to develop a very large database of scientific terms and statements across a broad corpus of published research and invention, including news, blogs, and other informal text as well as unpublished opinions. Intellectual MeritScientific evaluation, from awarding grants to reviewing tenure, has historically relied on quantity to proxy for quality. Progress is inferred from the amount of research produced or the sum of attention garnered. Numbers of books, articles, pages, citations and media mentions are tallied. These quantities are inexpensive to measure, but fail to directly capture whether a contribution is important. This project advances the measurement of scientific achievement by placing scientific claims in the context of past science. It does this by building on recent advances in computational language understanding, and the electronic availability of science. In particular, the project extracts scientific term and statements from a broad collection of published articles, patents and blogs in disciplines related to chemistry. These statements are supplemented with information about their social context -- their location in the network of authors and the geographical sprawl of global research institutions. Models are then developed that exploit patterns in the structure of scientific language to assess the importance of scientific programs and fields. The degree of innovation in science is assessed from the novelty and popularity of terms and statements within the broader network. The integration of scientific knowledge is assessed by examining the term and statement linkage, as well as repetition and elaboration patterns. These are, in turn, used to predict the path from science to Technology. The project also develops new methods for managing and processing large quantities of text and network data.Broader Impacts: The project develops general methods relevant for policy makers and scientists. This research generates, for example, high resolution, dynamic maps of knowledge claims in chemistry and neighboring disciplines such as pharmaceuticals and toxicology. The interactive nature of the maps means that they can serve as a teaching tool to help students understand scientific trends in their corner of science. They can also facilitate precise analysis of the production of science and stimulate the production of new hypotheses, as researchers note statements not made within the network of claims. When these maps are combined with the scientific models, they hold the potential to revolutionize the way scientists collaborate, identify research problems and validate hypotheses. Finally, because the research both clarifies what is published and where, as well as traces the careers of scientists and inventors,the research generates insights into what factors channel scientific attention, and how these factors can be harnessed to guide the most powerful public investments in innovation.

该项目为评价科技前景提供了新的途径。基本的观点是，科学概念，就像生态系统中的有机体一样，只存在于支持思想的网络中，这是理解科学概念被采用和传播的方式的关键。所研究的特定学科是化学及其相关学科。这项工作分为三个阶段。首先，它从科学网络中术语和陈述的新颖性和受欢迎程度来评估和预测科学创新。其次，它从术语和语句的联系、重复和阐述模式来评估和预测科学知识的整合。最后，它通过将术语和语句与问题联系起来，评估和预测从科学到技术的道路上的成功。这项研究使用网络工具开发了一个非常大的科学术语和陈述数据库，涵盖了广泛的已发表的研究和发明语料库，包括新闻、博客和其他非正式文本以及未发表的观点。从授予拨款到审查终身教职，科学评估历来依赖于数量而不是质量。进步是根据研究成果的数量或获得的关注的总和来推断的。统计书籍、文章、页面、引用和媒体提及的数量。这些数量的测量成本不高，但无法直接捕获贡献是否重要。该项目通过将科学主张置于过去科学的背景下，推进了对科学成就的衡量。它通过建立在计算机语言理解和科学的电子可用性方面的最新进展来实现这一点。特别是，该项目从与化学有关的学科的广泛的已发表的文章、专利和博客中提取科学术语和陈述。这些陈述还补充了关于它们的社会背景的信息——它们在作者网络中的位置和全球研究机构的地理分布。然后开发模型，利用科学语言结构中的模式来评估科学项目和领域的重要性。科学创新的程度是根据术语和陈述在更广泛的网络中的新颖性和受欢迎程度来评估的。科学知识的整合是通过检查术语和陈述联系，以及重复和阐述模式来评估的。反过来，这些又被用来预测从科学到技术的道路。该项目还开发了管理和处理大量文本和网络数据的新方法。更广泛的影响：该项目开发了与决策者和科学家相关的一般方法。例如，这项研究生成了化学和药学和毒理学等邻近学科知识要求的高分辨率动态地图。这些地图的互动性意味着它们可以作为一种教学工具，帮助学生在他们的科学领域了解科学趋势。它们还可以促进对科学成果的精确分析，并刺激新假设的产生，因为研究人员注意到在主张网络中没有做出的陈述。当这些地图与科学模型相结合时，它们有可能彻底改变科学家合作的方式，识别研究问题和验证假设。最后，由于该研究既澄清了发表的内容和地点，又追溯了科学家和发明家的职业生涯，因此该研究对引导科学注意力的因素以及如何利用这些因素来引导最有力的公共创新投资产生了深刻的见解。