Complex Query Answering (CQA) is a challenge task of Knowledge Graph (KG).
Due to the incompleteness of KGs, query embedding (QE) methods have been
proposed to encode queries and entities into the same embedding space, and
treat logical operators as neural set operators to obtain answers. However,
these methods train KG embeddings and neural set operators concurrently on both
simple (one-hop) and complex (multi-hop and logical) queries, which causes
performance degradation on simple queries and low training efficiency. In this
paper, we propose Query to Triple (Q2T), a novel approach that decouples the
training for simple and complex queries. Q2T divides the training into two
stages: (1) Pre-training a neural link predictor on simple queries to predict
tail entities based on the head entity and relation. (2) Training a query
encoder on complex queries to encode diverse complex queries into a unified
triple form that can be efficiently solved by the pretrained neural link
predictor. Our proposed Q2T is not only efficient to train, but also modular,
thus easily adaptable to various neural link predictors that have been studied
well. Extensive experiments demonstrate that, even without explicit modeling
for neural set operators, Q2T still achieves state-of-the-art performance on
diverse complex queries over three public benchmarks.

通过将训练分为两个阶段，分别在简单查询和复杂查询上进行训练，我们提出了一种名为 Q2T 的新方法，通过预测尾部实体来进行简单查询的预训练，并在复杂查询上训练查询编码器，将不同的复杂查询编码为统一的三元组形式，从而在不需要显式建模神经集合运算符的情况下，在三个公共基准测试上实现了最先进的性能。