USAILUIGIX: A Framework for Semantically Querying Filmic Media via Ontological Anchoring and RDF-star Knowledge Graphs

The exponential growth of audiovisual data has created a critical need for systems that can understand and index content at a semantic level, moving beyond simple metadata tagging. This work presents USAILUIGIX, a comprehensive framework that addresses this challenge by performing deep semantic encoding of filmic media. The system architecture is built to transform a linear, passive video stream into a dynamic, multi-layered, and machine-readable knowledge graph.

The methodology involves a dual-stream analysis pipeline:

1. Visual Stream Analysis: Individual frames are processed by a multimodal AI model (e.g., BLIP) to generate a holistic caption. This caption is then parsed using Natural Language Processing techniques to extract a canonical Subject-Predicate-Object (SVO) triple, representing the frame's core action or state.

2. Auditory Stream Analysis: The film's audio track is transcribed using a robust speech recognition model (e.g., Whisper) to produce time-stamped textual data corresponding to dialogue and significant sound events.

The central innovation of USAILUIGIX lies in its knowledge representation strategy. We leverage RDF-star (RDF)*, a crucial extension of the RDF standard, to model the extracted information. This choice overcomes the well-known limitations of standard RDF reification, allowing us to elegantly and directly annotate semantic triples with essential metadata. For instance, a triple such as <<:man :throws :ball>> can be directly annotated with its source frame (usgx:extractedFrom :frame_001), the natural language caption it was derived from, and a model-generated confidence score. This creates a rich, context-aware knowledge graph where every piece of information is explicitly linked to its origin.

The populated graph can be interrogated using SPARQL-star, enabling sophisticated, content-based queries that are impossible with conventional media analysis tools. This framework serves as a robust proof-of-concept for the symbol grounding problem in a real-world, multimodal context and provides a foundational tool for a new generation of applications in computational film studies, intelligent archival systems, and AI-driven narrative analysis. All components of this research, including the Python source code, the formal OWL ontology, and experimental results, are made available to ensure full transparency and reproducibility.