Fuzzy Logic Based Logical Query Answering on Knowledge Graphs

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