  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.
