Link prediction is an important task in addressing the incompleteness problem
of knowledge graphs (KG). Previous link prediction models suffer from issues
related to either performance or explanatory capability. Furthermore, models
that are capable of generating explanations, often struggle with erroneous
paths or reasoning leading to the correct answer. To address these challenges,
we introduce a novel Neural-Symbolic model named FaSt-FLiP (stands for Fast and
Slow Thinking with Filtered rules for Link Prediction task), inspired by two
distinct aspects of human cognition: "commonsense reasoning" and "thinking,
fast and slow." Our objective is to combine a logical and neural model for
enhanced link prediction. To tackle the challenge of dealing with incorrect
paths or rules generated by the logical model, we propose a semi-supervised
method to convert rules into sentences. These sentences are then subjected to
assessment and removal of incorrect rules using an NLI (Natural Language
Inference) model. Our approach to combining logical and neural models involves
first obtaining answers from both the logical and neural models. These answers
are subsequently unified using an Inference Engine module, which has been
realized through both algorithmic implementation and a novel neural model
architecture. To validate the efficacy of our model, we conducted a series of
experiments. The results demonstrate the superior performance of our model in
both link prediction metrics and the generation of more reliable explanations.

FaSt-FLiP 是一种新颖的神经逻辑模型，旨在结合逻辑和神经模型，以提高链接预测的性能，并生成更可靠的解释。

仿真人脑思考：一种具有快速和慢速推理以及过滤规则的神经符号链接预测模型

Emulating the Human Mind: A Neural-symbolic Link Prediction Model with  Fast and Slow Reasoning and Filtered Rules

Answering complex first-order logic (FOL) queries on knowledge graphs is a
fundamental task for multi-hop reasoning. Traditional symbolic methods traverse
a complete knowledge graph to extract the answers, which provides good
interpretation for each step. Recent neural methods learn geometric embeddings
for complex queries. These methods can generalize to incomplete knowledge
graphs, but their reasoning process is hard to interpret. In this paper, we
propose Graph Neural Network Query Executor (GNN-QE), a neural-symbolic model
that enjoys the advantages of both worlds. GNN-QE decomposes a complex FOL
query into relation projections and logical operations over fuzzy sets, which
provides interpretability for intermediate variables. To reason about the
missing links, GNN-QE adapts a graph neural network from knowledge graph
completion to execute the relation projections, and models the logical
operations with product fuzzy logic. Experiments on 3 datasets show that GNN-QE
significantly improves over previous state-of-the-art models in answering FOL
queries. Meanwhile, GNN-QE can predict the number of answers without explicit
supervision, and provide visualizations for intermediate variables.

本文提出了一种名为 GNN-QE 的神经符号模型，用于回答复杂的一阶逻辑查询，在解决神经方法推理难以解释的问题的同时，取得了较显著的实验成果。