The open-source SUMMA Platform is a highly scalable distributed architecture for monitoring a large number of media broadcasts in parallel, with a lag behind actual broadcast time of at most a few minutes. The Platform offers a fully automated media ingestion pipeline capable of recording live broadcasts, detection and transcription of spoken content, translation of all text (original or transcribed) into English, recognition and linking of Named Entities, topic detection, clustering and crosslingual multi-document summarization of related media items, and last but not least, extraction and storage of factual claims in these news items. Browser-based graphical user interfaces provide humans with aggregated information as well as structured access to individual news items stored in the Platform's database. This paper describes the intended use cases and provides an overview over the system's implementation.
Abstract. There have been several attempts to visualize OWL ontologies with UML style diagrams. Unlike ODM approach of defining a UML profile for OWL, we propose an extension to UML class diagrams (hard extension) that allows a more compact OWL visualization. The compactness is achieved through the native power of UML class diagrams extended with optional Manchester encoding for class expressions thus avoiding many explicit anonymous classes typical in ODM. We have implemented the proposed compact visualization in a UML style graphical editor for OWL 2. The editor contains a rich set of graphical layout algorithms for automatic ontology visualization, search facilities, graphical refactoring and interoperability with Protégé 4.
The open-source SUMMA Platform is a highly scalable distributed architecture for monitoring a large number of media broadcasts in parallel, with a lag behind actual broadcast time of at most a few minutes. It assembles numerous state-of-the-art NLP technologies into a fully automated media ingestion pipeline that can record live broadcasts, detect and transcribe spoken content, translate from several languages (original text or transcribed speech) into English, 1 recognize Named Entities, detect topics, cluster and summarize documents across language barriers, and extract and store factual claims in these news items. This paper describes the intended use cases and discusses the system design decisions that allowed us to integrate state-of-theart NLP modules into an effective workflow with comparatively little effort.
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