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

这个小企业创新研究（SBIR）第二阶段项目旨在建立一种基于单元的方法，从科学和技术文档中检索定量数据，是一种强大的替代关键字和基于文档的搜索模型。数据提取和上下文化的关键字方法受到限制，因为语义上下文化很差，而且数量通常以各种数字和单位格式书写。所提出的可靠数字数据提取方法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，以显著提高关键字方法的搜索召回率。所得到的数字-单位对将锚定索引，以实现具有高语义精度的高效科学探索性搜索，但不会过度依赖复杂的强加语义本体。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谱，对基于概率方法的模糊结果进行评分。该方法有望提高上下文数字数据提取的精度和召回率。反过来，由此产生的搜索引擎将使集体技术景观和趋势的即时可视化和分析成为可能，这将为索引文档所代表的任何技术领域的研究人员提供指导。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有效地提取数字数据。这些非结构化文档集包含大量潜在的定量数据，如果对这些数据进行适当的提取和聚合，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，该模型允许非专业用户快速汇总数千个相关数据点，只需输入简单的关键字，无需费力地打开和解析单个文档。因此，研究人员和学生可以探索以前无法访问的数据集，或者只有某个领域的专家才知道。这也将有助于在大型非结构化数据库中发现知识，因为表面上完全不同的变量之间的模式和相关性可以立即可视化。该平台将提供有效生成技术景观、预测新兴趋势和识别竞争技术异常值的能力。如果成功，这将对高科技产业创新有价值，包括参与研发的工程师、业务开发主管和知识产权管理人员，他们专注于技术参数空间内的资产配置、新技术风险、现有技术和专利侵权。

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.