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。

使用一次传递推理的逻辑信息传递网络在原子公式上

Logical Message Passing Networks with One-hop Inference on Atomic  Formulas

This paper presents a novel reranking method to better choose the optimal
query graph, a sub-graph of knowledge graph, to retrieve the answer for an
input question in Knowledge Base Question Answering (KBQA). Existing methods
suffer from a severe problem that there is a significant gap between top-1
performance and the oracle score of top-n results. To address this problem, our
method divides the choosing procedure into two steps: query graph ranking and
query graph reranking. In the first step, we provide top-n query graphs for
each question. Then we propose to rerank the top-n query graphs by combining
with the information of answer type. Experimental results on two widely used
datasets show that our proposed method achieves the best results on the
WebQuestions dataset and the second best on the ComplexQuestions dataset.

本文提出了一种新的重排方法，以更好地选择知识图谱的子图查询图形，以检索知识库问答（KBQA）中的输入问题的答案。

知识库问答中的查询图重新排名方法

A Method of Query Graph Reranking for Knowledge Base Question Answering

This paper presents a novel approach based on semantic parsing to improve the
performance of Knowledge Base Question Answering (KBQA). Specifically, we focus
on how to select an optimal query graph from a candidate set so as to retrieve
the answer from knowledge base (KB). In our approach, we first propose to
linearize the query graph into a sequence, which is used to form a sequence
pair with the question. It allows us to use mature sequence modeling, such as
BERT, to encode the sequence pair. Then we use a ranking method to sort
candidate query graphs. In contrast to the previous studies, our approach can
efficiently model semantic interactions between the graph and the question as
well as rank the candidate graphs from a global view. The experimental results
show that our system achieves the top performance on ComplexQuestions and the
second best performance on WebQuestions.

该研究提出了一种基于语义分析的新方法，以提高知识库问答（KBQA）的性能。该方法侧重于如何从候选集中选择最佳查询图，以从知识库（KB）中检索答案，实验结果证明，该系统在复杂问题上取得了最佳表现，并在 WebQuestions 上取得了第二佳表现。