公开(公告)号：US20160292573A1

公开(公告)日：2016-10-06

申请号：US15035555

申请日：2015-09-08

Applicant: BAE SYSTEMS INFORMATION AND ELECTRONIC SYSTEMS INTEGRATION INC.

Inventor： Olga BABKO-MALAYA ,  Daniel B. HUNTER ,  Andrew C. SEIDEL ,  Michelle A. TORRELLI

IPC: G06N5/02 ,  G06N7/00 ,  G06F17/30

CPC classification number: G06N5/022 ,  G06F16/24578 ,  G06F16/93 ,  G06N7/005

Abstract: The present invention is a method for constructing a knowledgebase that can provide analysis and trend prediction of emerging technologies. Metadata and full text are gathered from collections of documents, which can include more than 10 million documents, and are used to build a heterogeneous network of elements related to themes such as technical emergence. Indicators and models are selected that identify network characteristics and trends of interest. The indicators can be derived by applying a combination of citation analyses, natural language processing, entity disambiguation, organization classification, and time series analyses. A metric can be used to evaluate indicator utility. A framework can be sued to generate and validate the indicators. The models can be derived using an automated process. Upon receipt of a query, the indicators and models can be used to apply a scoring process to extracted features to predict a future prominence of an entity.

Abstract translation: 本发明是构建可以提供新兴技术的分析和趋势预测的知识库的方法。 元数据和全文都是从文档集合中收集的，其中可以包含超过1000万个文档，并用于构建与技术出现等主题相关的异构网络。 选择指标和模型，以确定感兴趣的网络特征和趋势。 指标可以通过引用分析，自然语言处理，实体消歧，组织分类和时间序列分析的组合来得出。 可以使用度量来评估指标效用。 可以起诉一个框架来生成和验证指标。 可以使用自动化过程来导出模型。 在接收到查询后，指标和模型可用于将评分过程应用于提取的特征以预测未来实体的突出。