Type-based Neural Link Prediction Adapter for Complex Query Answering
This addresses a fundamental challenge in multi-hop reasoning for knowledge graph applications, but it is incremental as it builds on existing link prediction methods by adding type information.
The paper tackles the problem of answering complex logical queries on incomplete knowledge graphs by incorporating type information, which existing methods often ignore, and proposes TENLPA to achieve state-of-the-art performance with improved generalization and robustness on three standard datasets.
Answering complex logical queries on incomplete knowledge graphs (KGs) is a fundamental and challenging task in multi-hop reasoning. Recent work defines this task as an end-to-end optimization problem, which significantly reduces the training cost and enhances the generalization of the model by a pretrained link predictors for query answering. However, most existing proposals ignore the critical semantic knowledge inherently available in KGs, such as type information, which could help answer complex logical queries. To this end, we propose TypE-based Neural Link Prediction Adapter (TENLPA), a novel model that constructs type-based entity-relation graphs to discover the latent relationships between entities and relations by leveraging type information in KGs. Meanwhile, in order to effectively combine type information with complex logical queries, an adaptive learning mechanism is introduced, which is trained by back-propagating during the complex query answering process to achieve adaptive adjustment of neural link predictors. Experiments on 3 standard datasets show that TENLPA model achieves state-of-the-art performance on complex query answering with good generalization and robustness.