SBIR Phase II: Units-based numeric data extraction with knowledge of scientific context

这一小型企业创新研究(SBIR)第二阶段项目旨在确定，以单位为基础的方法从科学和技术文件中检索定量数据是基于关键字和文件的搜索模型的有力替代方案。由于语义上下文不佳，而且数量通常以各种数字和单位格式书写，因此数据提取和上下文化的关键字方法有限。提出的可靠数字数据提取方法首先从数量智能索引开始，该索引识别许多数字格式并将数量转换为标准化的基本单位标记，以显著提高搜索查全率。生成的数字-单元对将锚定索引，以实现高语义精度的高效科学探索性搜索，但不过度依赖复杂的强制语义本体。研究将集中在一种专有的搜索时间数据评分算法上，该算法利用上下文敏感的数字频谱，基于概率方法对原本不明确的结果进行评分。这种方法有望提高上下文数字数据提取的精确度和召回率。反过来，由此产生的搜索引擎将能够实现对集体技术前景和趋势的即时可视化和分析，这将指导索引文件所代表的任何技术领域的研究人员。该项目的更广泛影响将是能够从科学文献和专利数据库等不同来源可靠和高效地提取数字数据。这些非结构化文档集包含丰富的潜在量化数据，如果适当地提取和聚合，可以实现强大的数据探索模式。基于单位的索引和数据评分算法是为探索性搜索模型定制的，该模型将允许非专家用户快速聚合数千个相关数据点，只需输入简单的关键字，而无需费力地打开和解析单个文件。因此，研究人员和学生可能会探索以前无法访问的数据集，或者只有某一领域的专家知道的数据集。这也将有助于在大型非结构化数据库中发现知识，因为看起来完全不同的变量之间的模式和相关性可以立即可视化。该平台将提供高效生成技术前景、预测新兴趋势和识别竞争性技术异常值的能力。如果成功，这对高科技产业创新将是有价值的，包括参与研发的工程师，以及专注于在技术参数空间内进行资产配置、新技术投资、现有技术和专利侵权的业务发展高管和智力资产经理。

This Small Business Innovation Research (SBIR) Phase II project aims to establish that a units-based approach to retrieving quantitative data from scientific and technical documents is a powerful alternative to keyword and document based search models. Keyword approaches to data extraction and contextualization are limited due to poor semantic contextualization and because quantities are often written in a wide variety of numeric and unit formats. The proposed approach to reliable numeric data extraction begins with quantity-intelligent indexing that recognizes many numeric formats and converts quantities to standardized base-unit tokens, to significantly enhance search recall over keyword approaches. The resulting number-unit pairs will anchor the index to enable efficient scientific exploratory search with high semantic precision, but without overly relying on sophisticated imposed semantic ontologies. Research will focus on a proprietary search-time data scoring algorithm that utilizes context-sensitive numeric spectra, to score otherwise ambiguous results based on probabilistic methods. This approach is expected to improve both precision and recall of contextual numeric data extraction. In turn, the resulting search engine will enable instant visualization and analysis of collective technology landscapes and trends, which will guide researchers in any area of technology represented by the indexed documents.The broader impact of this project will be to enable reliable and efficient extraction of numeric data from diverse sources such as scientific literature and patent databases. These unstructured document sets contain a wealth of latent quantitative data which, if properly extracted and aggregated, can enable powerful modes of data exploration. The unit-based index and data-scoring algorithm are customized for an exploratory search model that will allow non-expert users to rapidly aggregate thousands of relevant data points, with simple keyword inputs and without laboriously opening and parsing individual documents. Researchers and students may thus explore data sets that were previously inaccessible, or known only to experts in a field. This will also contribute to knowledge discovery within large unstructured databases, since patterns and correlations between seemingly disparate variables can be immediately visualized. The platform will provide the capability to efficiently generate technology landscapes, anticipate emerging trends, and recognize competitive technical outliers. If successful, this will be valuable for high-tech industrial innovation including for engineers involved in R&D as well as business development executives and intellectual asset managers who focus on asset allocation, new technology ventures, prior art and patent infringement within a technical parameter space.