AI 中文总结
本文针对语义物联网数字孪生平台缺乏三维相似度搜索能力的问题，提出多模态嵌入框架，在Thing'in平台实现后经S3DIS验证，可支持混合本体-向量查询，满足相关领域的三维相似度搜索需求。
AI 中文摘要
语义物联网（SWoT）平台将物理基础设施建模为针对领域本体分类的知识图谱，支持表达性的结构化与逻辑查询，但缺乏严格本体等价之外表达相似度的原生机制，这是电信基础设施、工业物联网等领域三维数字孪生的关键缺口，这类领域的查询需结合本体约束与异构、随时间演化的场景数据的多模态相似度搜索。本文提出一种框架，为SWoT平台扩展多模态嵌入层：将包含三维点云、时间属性、语义标签的本体分类实体编码为与知识图谱一同存储的潜在向量表示，支持结合基于图的过滤与相似度搜索的混合本体-向量查询。该框架在Orange Research的Thing'in平台与Clock-G时间图数据库上实现，基于S3DIS的可行性评估表明，图过滤能在时间与关系约束下有效限制搜索池，通用预训练编码器生成的表示足以用于相似度检索，也可作为下游预测任务的初步编码步骤。
英文摘要
Semantic Web of Things (SWoT) platforms model physical infrastructure as knowledge graphs typed against domain ontologies, enabling expressive structural and logical queries. However, they lack native mechanisms to express similarity beyond strict ontological equivalence, which represents a critical gap for 3D digital twins in domains such as telecom infrastructure and industrial IoT, where queries must combine ontological constraints with multimodal similarity search over heterogeneous, temporally-evolving scene data. We propose a framework that extends SWoT platforms with a multimodal embedding layer: ontology-typed entities comprising 3D point clouds, temporal attributes, and semantic labels are encoded into latent vector representations stored alongside the knowledge graph, enabling hybrid ontology-vector queries that combine graph-based filtering with similarity search. Implemented on Orange Research's Thing'in platform with the Clock-G temporal graph database, a feasibility evaluation on S3DIS demonstrates that graph filtering effectively restricts the search pool under temporal and relational constraints, and that general-purpose pretrained encoders produce representations sufficient for similarity retrieval and as a preliminary encoding step for downstream predictive tasks.
Journal ref4th International Workshop on the Semantic WEb of EveryThing (SWEET 2026), co-located with ICWE 2026, Jun 2026, Lyon, France
