Fuzzy Logic Based Logical Query Answering on Knowledge Graphs


  • Xuelu Chen University of California, Los Angeles
  • Ziniu Hu University of California, Los Angeles
  • Yizhou Sun University of California, Los Angeles




Data Mining & Knowledge Management (DMKM), Machine Learning (ML)


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.




How to Cite

Chen, X., Hu, Z., & Sun, Y. (2022). Fuzzy Logic Based Logical Query Answering on Knowledge Graphs. Proceedings of the AAAI Conference on Artificial Intelligence, 36(4), 3939-3948. https://doi.org/10.1609/aaai.v36i4.20310



AAAI Technical Track on Data Mining and Knowledge Management