NeuroSymActive: A Differentiable Neural-Symbolic Framework for KGQA
NeuroSymActive presents a modular approach for Knowledge Graph Question Answering, merging a differentiable neural-symbolic reasoning component with an active exploration controller. This framework tackles the difficulty of merging graph structures with neural models by integrating soft-unification symbolic modules, a neural path evaluator, and a Monte-Carlo exploration strategy that emphasizes valuable path expansions. Its goal is to address the inefficiencies and vulnerabilities associated with simply embedding graph facts into prompts, as well as the high expenses linked to purely symbolic or search-intensive techniques. This framework is detailed in a paper available on arXiv (2602.15353) and is designed for knowledge-intensive inquiries that necessitate accurate, structured multi-hop reasoning.
Key facts
- NeuroSymActive is introduced in arXiv paper 2602.15353.
- It combines a differentiable neural-symbolic reasoning layer with an active exploration controller.
- The framework uses soft-unification symbolic modules, a neural path evaluator, and a Monte-Carlo style exploration policy.
- It targets Knowledge Graph Question Answering for knowledge-intensive queries.
- The method aims to improve efficiency over naive embedding of graph facts into prompts.
- It also aims to reduce retrieval costs compared to purely symbolic or search-heavy approaches.
- The exploration policy prioritizes high-value path expansions.
- The paper is categorized as a replace-cross announcement.
Entities
Institutions
- arXiv