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.

翻译：复杂查询回答（CQA）是知识图谱（KG）中的一项挑战性任务。由于知识图谱的不完整性，查询嵌入（QE）方法被提出，将查询和实体编码到同一嵌入空间中，并将逻辑算子视为神经集算子以获取答案。然而，这些方法同时在简单查询（单跳）和复杂查询（多跳及逻辑查询）上训练知识图谱嵌入和神经集算子，导致简单查询性能下降且训练效率低下。本文提出Query to Triple（Q2T）这一新颖方法，将简单查询与复杂查询的训练解耦。Q2T将训练分为两个阶段：（1）在简单查询上预训练神经链接预测器，根据头实体和关系预测尾实体；（2）在复杂查询上训练查询编码器，将多样化的复杂查询编码为统一的三元组形式，从而可由预训练的神经链接预测器高效求解。所提出的Q2T不仅训练高效，且具有模块化特性，易于适配各类已深入研究的神经链接预测器。大量实验表明，即使未显式建模神经集算子，Q2T在三个公开基准测试的多样化复杂查询上仍达到了最先进的性能。