Fuzzy Spatiotemporal Knowledge Graph Queries via Global and Local Uncertain Dynamic Subgraph Reasoning Using Embedding
Hao Ji, Li Yan, Zongmin Ma · IEEE Transactions on Fuzzy Systems · 2025
A large amount of work focus on static and temporal knowledge graph (KG) queries via subgraph reasoning, but it fails to consider the space-sensitivity and fuzziness of KG. Subgraph query in fuzzy spatiotemporal knowledge graph (f-STKG) is a fundamental and challenging task. Although a few efforts exist dedicated tof-STKG queries based on subgraph matching, they are sensitive to missing information in the knowledge base. To this end, we propose thef-STKG query method by subgraph reasoning using embeddings. The method aims to capture the semantic similarity between the query subgraph and candidate subgraph off-STKG to accomplish subgraph matching. We first embedf-STKGs into vector space to formf-STKG vectors. Then, we construct a global subgraph off-STKG using Composition-based Graph Convolutional Neural Networks (CompGCN), which allows global information adjacent to this subgraph to be integrated. Subsequently, the query subgraph off-STKG is constructed using mean pooling and Gated Recurrent Unit (GRU), as well as the candidate subgraph of thef-STKG is constructed using CompGCN and GRU. This facilitates the integration of structural and sequence pattern information of subgraphs. Finally, we measured the semantic similarity between this query subgraph and the candidate subgraph using the subgraph matching function. In addition, we designed experiments to validate the effectiveness of our method on two datasets. Results show that our method outperforms state-of-the-art baselines.