Entity replacement strategy for temporal knowledge graph query relaxation.
Journal:
Neural networks : the official journal of the International Neural Network Society
Published Date:
May 4, 2025
Abstract
The temporal knowledge graph (TKG) query enables the retrieval of candidate answer lists by addressing questions that involve temporal constraints, regarded as a crucial downstream task in the realm of the temporal knowledge graph. Existing methods primarily focus on the TKG queries of non-empty results, while neglecting the consideration of TKG queries that return empty results. Therefore, there is still potential for enhancing the flexibility of queries. In this paper, we propose an Entity Replacement strategy for Temporal knowledge graph Query Relaxation (ER-TQR), a flexible relaxation method for TKG queries targeting empty results based on an entity replacement strategy. ER-TQR distinguishes itself from existing query relaxation techniques replacing incompatible entities with semantically and temporally aligned candidates, minimizing distortion of original queries. For the query embedding, we leverage an embedding method based on the Bidirectional Encoder Representations from Transformers (BERT) model, which significantly improves the semantic representation ability. Concurrently, we use the Bidirectional Gated Recurrent Unit (Bi-GRU) model to assess the chance of each entity appearing with errors and decide if it needs to be replaced. To uphold the original intent of the query, we replace the entities based on similarity calculation and generate relaxed query results. The experimental results show that our method outperforms existing query relaxation methods in 4 out of 5 metrics on different datasets.