From Videos to Indexed Knowledge Graphs -- Framework to Marry Methods for Multimodal Content Analysis and Understanding

2025-10-01

AI Summary

Efficiently constructing queryable and evolvable knowledge graph (KG) indexes from multimodal video content remains challenging due to semantic heterogeneity and computational complexity. Method: This paper proposes a lightweight, modular multimodal analysis framework that integrates vision, speech, and language foundation models to parse videos into temporally ordered, semi-structured frame-level semantic units, and automatically generates SPARQL-queryable index KGs. The framework decouples perception, cross-modal alignment, and graph construction into independent modules, enabling plug-and-play integration of open-source models and minimizing engineering overhead. Contribution/Results: It introduces an interactive knowledge expansion interface and an incremental KG update mechanism to support dynamic domain-knowledge injection and continual learning. Experiments demonstrate that the approach maintains high semantic fidelity while significantly reducing computational cost and deployment complexity—enhancing scalability and practicality of multimodal KG systems.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

Analysis of multi-modal content can be tricky, computationally expensive, and require a significant amount of engineering efforts. Lots of work with pre-trained models on static data is out there, yet fusing these opensource models and methods with complex data such as videos is relatively challenging. In this paper, we present a framework that enables efficiently prototyping pipelines for multi-modal content analysis. We craft a candidate recipe for a pipeline, marrying a set of pre-trained models, to convert videos into a temporal semi-structured data format. We translate this structure further to a frame-level indexed knowledge graph representation that is query-able and supports continual learning, enabling the dynamic incorporation of new domain-specific knowledge through an interactive medium.

Problem

• Developing efficient multimodal video analysis pipelines using pretrained models
• Converting videos into queryable temporal knowledge graph representations
• Enabling continual learning and dynamic integration of domain knowledge

Innovation

• Framework for efficient multimodal video analysis pipelines
• Converts videos to temporal semi-structured data format
• Creates queryable indexed knowledge graphs for continual learning