OSOTIS - Kollaborative inhaltsbasierte Video-Suche

Die Video-Suchmaschine OSOTIS ermöglicht eine automatische inhaltsbezogene Annotation von Videodaten und dadurch eine zielgenaue Suche auch innerhalb einzelner Videoaufzeichnungen. Neben objektiv gewonnenen zeitabhängigen Deskriptoren, die über eine automatische Synchronisation von ggf. zusätzlich vorhandenem textbasiertem Material mit den vorliegenden Videodaten gewonnen werden, können kollaborativ zusätzlich eigene, zeitbezogene Schlagwörter (Tags) und Kommentare innerhalb eines Videos vergeben werden (sequentielles Tagging), die zur Implementierung einer verbesserten und personalisierten Suche dienen

Le harcelement des majoritaires

Available at INIST (FR), Document Supply Service, under shelf-number : DO 4415 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueSIGLEFRFranc

Linked stage graph

Archives today are publishing their cultural heritage data on the Web for exploration. However, for archive novices the traditional archival structures often are not intuitive and difficult to understand, and thus challenges data access and consumption. To tackle this problem, Linked Stage Graph was developed, a knowledge graph (KG) on the foundation of historical data about the Stuttgart State Theater. The data was made available by the Baden-Wuerttemberg State Archives for the Coding da Vinci hackathon. This demo paper contributes the KG,a SPARQL endpoint, named entity extraction and linking to existing authoritative KGs as well as a dedicated user interface for exploration

A preliminary investigation towards improving linked data quality using distance-based outlier detection

With more and more data being published on the Web as Linked Data, Web Data quality is becoming increasingly important. While quite some work has been done with regard to quality assessment of Linked Data, only few works have addressed quality improvement. In this article, we present a preliminary an approach for identifying potentially incorrect RDF statements using distance-based outlier detection. Our method follows a three stage approach, which automates the whole process of finding potentially incorrect statements for a certain property. Our preliminary evaluation shows that a high precision is maintained with different settings