Fuzzy Logic Based Logical Query Answering on Knowledge Graphs
This addresses the challenge of logical query answering on knowledge graphs for AI applications, offering a novel approach that reduces the need for extensive training data.
The paper tackles the problem of answering complex First-Order Logical queries on incomplete knowledge graphs by introducing FuzzQE, a fuzzy logic-based framework that defines logical operators in a principled, learning-free manner, resulting in significantly better performance compared to state-of-the-art methods on benchmark datasets.
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.