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Intro -- 1 Einleitung -- 1.1 Beispiel: Filmaufnahmen von der Bundesgartenschau -- 1.2 Herausforderungen für die Medienunternehmen -- 1.3 Lösungsansatz: semantisches Medienmanagement -- 1.4 Aufbau der Arbeit -- 2 Einführung in das Medienmanagement -- 2.1 Dokumentarischer Prozess -- 2.2 Management digitaler Medien -- 3 Problemanalyse und Lösungsansatz -- 4 Einflussfaktoren für Semantik und ihre Abhängigkeiten -- 5 Anforderungen an die Beschreibung digitaler Medien -- 6 Beschreibung von Medieninhalten mit Ontologien -- 6.1 Ontologien und Linked Data -- 6.2 Beschreibung von Medien mit Kernontologien und Linked Data -- 7 Prozessinnovation im semantischen Medienmanagement -- 7.1 Einfachere und kostengünstigere Erschließung der Medieninhalte -- 7.2 Höhere Sichtbarkeit der Medienbestände im Internet -- 7.3 Facettierte Suche und Navigation -- 7.4 Integration von Prozessen und Verknüpfung von Medienbeständen -- 8 Zusammenfassung.

Background: Psychiatry's evidence-, implementation-, and treatment-gaps. Aims: The aim of this study is to uncover current trends and gaps in psychiatric research. Understanding where psychiatric research is going and where there might be blind spots is important to better align it with global mental health challenges and with service users' needs. Method: 10 top-ranking scientific journals (highest impact factors) in psychiatry were identified for 3 years (1999, 2009, 2019) using Clarivate Analytics. Metadata of all papers published by these journals in the index years were downloaded, and the relevance and relatedness of terms from all titles and abstracts were computed and visualized using VOSviewer. Results: In 1999, prominent themes included schizophrenia and novel antipsychotics as well as research on families. Ten and 20 years later, neurobiological research, especially genetic and animal studies, had gained importance. Social and psychological themes were less present across all three time points. Conclusions: In high-ranking psychiatric journals, neurobiological research appears to gain importance while social themes are under-represented. In view of challenges such as implementation gaps, marginalization of people with severe mental illness and mental health risks through social inequality, there seems to be a dissociation between research and patient needs. We suggest a systems approach to bring together different kinds of knowledge.

This is the evaluation result from the experiment of the paper:

C. Nishioka and A. Scherp "Profiling vs. Time vs. Content: What does Matter for Top-k
Publication Recommendation based on Twitter Profiles?", JCDL, 2016.

The paper reported the experiment of the scientific publication recommender system.

The dataset contains evaluations of the recommended scientific publications made by anonymized 123 participants in the experiment.

The user-study evaluates a vocabulary term recommendation service that is based on how other data providers have used RDF classes and properties in the Linked Open Data cloud. The study compares the machine learning technique Learning to Rank (L2R), the classical data mining approach Association Rule mining (AR), and a baseline that does not provide any recommendations. This data collection comprises the raw results of this user-study in SPSS format.

The main contribution of the survey is to condense and aggregate the experience and knowledge of Linked Data experts and practitioners regarding the most common strategies to reuse vocabularies when modeling Linked Open Data in a real-world scenario.