ITR: Text Metadata Mining: Extending the Frontiers of Text Based Applications in Biomedicine

ITR：文本元数据挖掘：扩展生物医学中基于文本的应用的前沿

• 批准号: 0312356
• 负责人: Padmini Srinivasan
• 金额: $ 31.23万
• 依托单位: University of Iowa
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-08-01 至 2007-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0312356&HistoricalAwards=false
• 关键词: ITR; Text; Metadata; Mining; Extending

The goal in text mining is automatic knowledge discovery from large text collections. The importance of text mining technology comes from its potential to enable the process of hypothesis generation and thereby influence the progress of science. This complex process, a crucial initial step for making scientific discoveries, relies on several factors including intangible ones such as prior experience and intuition. Oftentimes chance connections made serendipitously, later turn out to be fruitful. Text mining tools automatically scour large text collections to identify a small set of hypotheses that are both novel and interesting enough to warrant further research by the user. This research is a study of text mining algorithms, especially those that exploit the metadata associated with text. The specific aims are (1) to implement algorithms designed around three basic text mining functions in a prototype hypothesis generation and knowledge discovery tool and (2) to evaluate this prototype through a set of mining experiments designed to explore knowledge discovery within the biomedical domain. Although biomedicine forms the topical context of this study, the mining functions explored are general and may be applied to any subject area that has a large text database with associated metadata.

文本挖掘的目标是从大量文本中自动发现知识。 文本挖掘技术的重要性来自于它的潜力，使假设生成的过程，从而影响科学的进步。 这个复杂的过程是科学发现的关键第一步，它依赖于几个因素，包括无形的因素，如先前的经验和直觉。 通常，偶然的机会联系，后来证明是富有成效的。 文本挖掘工具自动搜索大型文本集合，以识别一小部分既新颖又有趣的假设，以保证用户进行进一步的研究。 本研究是一个文本挖掘算法的研究，特别是那些利用与文本相关的元数据。 具体目标是（1）实现算法设计围绕三个基本的文本挖掘功能的原型假设生成和知识发现工具和（2）通过一组挖掘实验，旨在探索生物医学领域内的知识发现，以评估这个原型。 虽然生物医学形成了本研究的主题背景，但探索的挖掘功能是通用的，可以应用于具有大型文本数据库和相关元数据的任何主题领域。