  Streamlining content discovery within media archives requires integrating
advanced data representations and effective visualization techniques for clear
communication of video topics to users. The proposed system addresses the
challenge of efficiently navigating large video collections by exploiting a
fusion of visual, audio, and textual features to accurately index and
categorize video content through a text-based method. Additionally, semantic
embeddings are employed to provide contextually relevant information and
recommendations to users, resulting in an intuitive and engaging exploratory
experience over our topics ontology map using OpenAI GPT-4.
