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。

Query2Triple: 统一查询编码以回答知识图谱上的多样复杂查询

Query2Triple: Unified Query Encoding for Answering Diverse Complex  Queries over Knowledge Graphs

Complex Query Answering (CQA) over Knowledge Graphs (KGs) has attracted a lot
of attention to potentially support many applications. Given that KGs are
usually incomplete, neural models are proposed to answer the logical queries by
parameterizing set operators with complex neural networks. However, such
methods usually train neural set operators with a large number of entity and
relation embeddings from the zero, where whether and how the embeddings or the
neural set operators contribute to the performance remains not clear. In this
paper, we propose a simple framework for complex query answering that
decomposes the KG embeddings from neural set operators. We propose to represent
the complex queries into the query graph. On top of the query graph, we propose
the Logical Message Passing Neural Network (LMPNN) that connects the local
one-hop inferences on atomic formulas to the global logical reasoning for
complex query answering. We leverage existing effective KG embeddings to
conduct one-hop inferences on atomic formulas, the results of which are
regarded as the messages passed in LMPNN. The reasoning process over the
overall logical formulas is turned into the forward pass of LMPNN that
incrementally aggregates local information to finally predict the answers'
embeddings. The complex logical inference across different types of queries
will then be learned from training examples based on the LMPNN architecture.
Theoretically, our query-graph represenation is more general than the
prevailing operator-tree formulation, so our approach applies to a broader
range of complex KG queries. Empirically, our approach yields the new
state-of-the-art neural CQA model. Our research bridges the gap between complex
KG query answering tasks and the long-standing achievements of knowledge graph
representation learning.

我们提出了一种简单的复杂查询方法，通过将复杂查询表示为查询图并使用逻辑信息传递神经网络，将知识图谱嵌入分解为本地推理和全局推理，实现了对各种类型的查询的复杂推理和新的最先进的神经查询应答模型。