Answering complex First-Order Logical (FOL) queries on large-scale incomplete
knowledge graphs (KGs) is an important yet challenging task. Recent advances
embed logical queries and KG entities in the same space and conduct query
answering via dense similarity search. However, most logical operators designed
in previous studies do not satisfy the axiomatic system of classical logic,
limiting their performance. Moreover, these logical operators are parameterized
and thus require many complex FOL queries as training data, which are often
arduous to collect or even inaccessible in most real-world KGs. We thus present
FuzzQE, a fuzzy logic based logical query embedding framework for answering FOL
queries over KGs. FuzzQE follows fuzzy logic to define logical operators in a
principled and learning-free manner, where only entity and relation embeddings
require learning. FuzzQE can further benefit from labeled complex logical
queries for training. Extensive experiments on two benchmark datasets
demonstrate that FuzzQE provides significantly better performance in answering
FOL queries compared to state-of-the-art methods. In addition, FuzzQE trained
with only KG link prediction can achieve comparable performance to those
trained with extra complex query data.

FuzzQE 采用模糊逻辑定义逻辑运算符，利用模糊的查询嵌入框架回答在知识图上的存在量化查询，比现有技术提供更好的性能。