NeuroSymActive: Differentiable Neural-Symbolic Reasoning with Active Exploration for Knowledge Graph Question Answering

TL;DR

NeuroSymActive is a neural-symbolic framework for knowledge graph question answering that improves accuracy and efficiency by combining differentiable reasoning with active exploration.

Contribution

It introduces a modular neural-symbolic reasoning layer with a value-guided exploration controller for KGQA, reducing graph lookups and model calls.

Findings

Achieves strong answer accuracy on KGQA benchmarks.

Reduces number of graph lookups compared to baselines.

Utilizes a Monte-Carlo exploration policy for high-value path expansion.

Abstract

Large pretrained language models and neural reasoning systems have advanced many natural language tasks, yet they remain challenged by knowledge-intensive queries that require precise, structured multi-hop inference. Knowledge graphs provide a compact symbolic substrate for factual grounding, but integrating graph structure with neural models is nontrivial: naively embedding graph facts into prompts leads to inefficiency and fragility, while purely symbolic or search-heavy approaches can be costly in retrievals and lack gradient-based refinement. We introduce NeuroSymActive, a modular framework that combines a differentiable neural-symbolic reasoning layer with an active, value-guided exploration controller for Knowledge Graph Question Answering. The method couples soft-unification style symbolic modules with a neural path evaluator and a Monte-Carlo style exploration policy that prioritizes high-value path expansions. Empirical results on standard KGQA benchmarks show that NeuroSymActive attains strong answer accuracy while reducing the number of expensive graph lookups and model calls compared to common retrieval-augmented baselines.