Problem
Research questions and friction points this paper is trying to address.
Multimodal Knowledge Graphs
Content-Agnostic
Media Isolation
MediaGraph: A Content-Aware Data Model and Query Framework for Multimodal Knowledge Graphs
📝 Abstract
Multimodal knowledge graphs typically treat multimedia documents as opaque, external entities. This content-agnostic approach constrains retrieval and analysis by isolating media from the graph's core structure, hindering the ability to capture and query complex relationships across media types. To address this, we introduce the MediaGraph Data Model and its prototypical implementation MeGraS, the MediaGraph Store, a novel approach that integrates multimedia content as graph nodes. This paradigm shift enables the query engine to directly access and process a document's intrinsic content, allowing for native operations such as feature-based similarity search, dynamic segmentation, and the inference of non-materialized relations. By extending the SPARQL query language, MeGraS provides a cohesive platform for the storage, management, and expressive querying of multimodal data. MeGraS is open-source software that establishes a new framework, moving the field toward content-aware multimodal knowledge graphs.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Content-Aware
Multimodal Knowledge Graphs
Feature-Based Similarity Search
Dynamic Segmentation
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