AI 中文总结
该研究提出MediaGraph数据模型及开源原型MeGraS，将多媒体内容作为图谱节点，扩展SPARQL实现多模态数据的存储、查询，推动内容感知多模态知识图谱发展。
AI 中文摘要
多模态知识图谱通常将多媒体文档视为不透明的外部实体，这种与内容无关的方法通过将媒体与图谱核心结构隔离，限制了检索与分析，阻碍了跨媒体类型复杂关系的捕获与查询能力。为解决该问题，我们提出MediaGraph数据模型及其原型实现MeGraS（MediaGraph Store），一种将多媒体内容集成图谱节点的新方法。该范式转变使查询引擎可直接访问并处理文档内在内容，支持基于特征的相似性搜索、动态分段及非物化关系推理等原生操作。通过扩展SPARQL查询语言，MeGraS为多模态数据的存储、管理与表达性查询提供了统一平台。MeGraS为开源软件，构建了新框架，推动该领域向内容感知多模态知识图谱发展。
英文摘要
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.
