UnRavL: A Neuro-Symbolic Framework for Answering Graph Pattern Queries in Knowledge Graphs

The challenge of answering graph queries over incomplete knowledge graphs is gaining significant attention in the machine learning community. Neuro-symbolic models have emerged as a promising approach, combining good performance with high interpretability. These models utilize trained architectures to execute atomic queries and integrate modules that mimic symbolic query operators. Most neuro-symbolic query processors, however, are either constrained to \ast tree-like\ast graph pattern queries or incur an extensive computational overhead. We introduce a framework for \ast efficiently\ast answering \ast arbitrary\ast graph pattern queries over incomplete knowledge graphs, encompassing both tree-like and cyclic queries. Our approach employs an approximation scheme that facilitates acyclic traversals for cyclic patterns, thereby embedding additional symbolic bias into the query execution process. Supporting general graph pattern queries is crucial for practical applications but remains a limitation for most current neuro-symbolic models. Our framework addresses this gap. Our experimental evaluation demonstrates that our framework performs competitively on three datasets, effectively handling cyclic queries through our approximation strategy. Additionally, it maintains the performance of existing neuro-symbolic models on anchored tree-like queries and extends their capabilities to queries with existentially quantified variables.