UIMA2LOD: Integrating UIMA text annotations into the linked open data cloud

Abstract

The LOD cloud is becoming the de-facto standard for sharing and connecting pieces of data, information and knowledge on the Web. As of today, means for the seamless integration of structured data into the LOD cloud are available. However, algorithms for integrating information enclosed in unstructured text sources are missing. In order to foster the (re)use of the high percentage of unstructured text, automatic means for the integration of their content are needed. We address this issue by proposing an approach for conceptual representation of textual annotations which distinguishes linguistic from semantic annotations and their integration. Additionally, we implement a generic UIMA pipeline that automatically creates a LOD graph from texts that (1) implements the proposed conceptual representation, (2) extracts semantically classified entities, (3) links to existing LOD datasets and (4) generates RDF graphs from the extracted information. We show the application and benefits of the approach in a case study on a medical corpus.