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: 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.