Modeling Relational Patterns for Logical Query Answering over Knowledge Graphs
This work addresses the challenge of logical query answering for knowledge graph applications, representing an incremental advance by incorporating relational patterns into existing query embedding frameworks.
The paper tackles the problem of answering first-order logical queries over incomplete knowledge graphs by introducing RoConE, a query embedding method that models relational patterns like symmetry and composition using geometric cones and rotations in complex space, achieving improved performance on benchmark datasets.
Answering first-order logical (FOL) queries over knowledge graphs (KG) remains a challenging task mainly due to KG incompleteness. Query embedding approaches this problem by computing the low-dimensional vector representations of entities, relations, and logical queries. KGs exhibit relational patterns such as symmetry and composition and modeling the patterns can further enhance the performance of query embedding models. However, the role of such patterns in answering FOL queries by query embedding models has not been yet studied in the literature. In this paper, we fill in this research gap and empower FOL queries reasoning with pattern inference by introducing an inductive bias that allows for learning relation patterns. To this end, we develop a novel query embedding method, RoConE, that defines query regions as geometric cones and algebraic query operators by rotations in complex space. RoConE combines the advantages of Cone as a well-specified geometric representation for query embedding, and also the rotation operator as a powerful algebraic operation for pattern inference. Our experimental results on several benchmark datasets confirm the advantage of relational patterns for enhancing logical query answering task.