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

TL;DR

This paper presents a neuro-symbolic framework capable of answering complex, cyclic, and existentially quantified graph pattern queries in knowledge graphs, addressing limitations of previous models that only handled tree-like queries.

Contribution

It introduces an approximation-based approach that extends neuro-symbolic query answering to arbitrary graph patterns, including cyclic and existentially quantified queries.

Findings

Performs competitively on multiple datasets

Handles cyclic graph patterns effectively

Extends capabilities to existentially quantified queries

Abstract

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. However, most neuro-symbolic query processors are constrained to tree-like graph pattern queries. These queries admit a bottom-up execution with constant values or anchors at the leaves and the target variable at the root. While expressive, tree-like queries fail to capture critical properties in knowledge graphs, such as the existence of multiple edges between entities or the presence of triangles. We introduce a framework for answering arbitrary graph pattern queries over incomplete knowledge graphs, encompassing both cyclic queries and tree-like queries with existentially quantified leaves. These classes of queries are vital for practical applications but are beyond the scope of most current neuro-symbolic models. Our approach employs an approximation scheme that facilitates acyclic traversals for cyclic patterns, thereby embedding additional symbolic bias into the query execution process. 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.