SSQTKG: A Subgraph-based Semantic Query Approach for Temporal Knowledge Graph

Real-world knowledge graphs are growing in size with the explosion of data and rapid expansion of knowledge. There are some studies on knowledge graph query, but temporal knowledge graph (TKG) query is still a relatively unexplored field. A temporal knowledge graph is a knowledge graph that contains temporal information and contains knowledge that is likely to change over time. It introduces a temporal dimension that can characterize the changes and evolution of entities and relationships at different points in time. However, in the existing temporal knowledge graph query, the entity labels are one-sided, which cannot accurately reflect the semantic relationships of temporal knowledge graphs, resulting in incomplete query results. For the processing of temporal information in temporal knowledge graphs, we propose a temporal frame filtering approach and measure the acceptability of temporal frames by the new definition sim_time based on the proposed three temporal frames and nine rules. For measuring the semantic relationship of predicates between entities, we vectorize the semantic similarity between predicates, i.e., edges, using the knowledge embedding model, and propose the new definition sim_pre to measure the semantic similarity of predicates. Based on these, we propose a new semantic temporal knowledge graph query method ${\mathrm{SSQ}}_{\mathrm{TKG}}$, and perform pruning operations to optimize the query efficiency of the algorithm based on connectivity. Extensive experiments show that ${\mathrm{SSQ}}_{\mathrm{TKG}}$ can return more accurate and complete results that meet the query conditions in the semantic query and can improve the performance of the querying on the temporal knowledge graph.