Neurosymbolic Retrievers for Retrieval-Augmented Generation

2026-01-01

IEEE Intelligent Systems

AI Summary

This work addresses the limited transparency and explainability of traditional Retrieval-Augmented Generation (RAG) systems, whose fully neural architectures hinder interpretability and debugging in high-stakes applications. To overcome this, the authors propose a novel paradigm that integrates symbolic reasoning with neural retrieval by deeply embedding knowledge graphs into the RAG pipeline. The approach introduces three key mechanisms: Knowledge-Modulated Alignment Retrieval (MAR), Knowledge Graph Path–enhanced Querying (KG-Path RAG), and Process Knowledge–infused Re-ranking (Process Knowledge-infused RAG). Evaluated on a mental health risk assessment task, the proposed framework significantly enhances both the transparency of retrieved evidence and overall system performance, demonstrating the benefits of synergistically combining structured symbolic knowledge with neural retrieval.

Technology Category

• Knowledge Representation and Reasoning: Knowledge Acquisition
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Cognitive Modeling & Cognitive Systems: Symbolic Representations

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

Abstract

Retrieval-augmented generation (RAG) has made significant strides in overcoming key limitations of large language models, such as hallucination, lack of contextual grounding, and issues with transparency. This new framework aims to answer two primary questions: 1) Can retrievers provide a clear and interpretable basis for document selection? 2) Can symbolic knowledge enhance the clarity of the retrieval process? We propose three methods to improve this integration. The first is modulation augmented retrieval which employs modulation networks to refine query embeddings using interpretable symbolic features, thereby making document matching more explicit. The second is KG-Path RAG, which enhances queries by traversing knowledge graphs to improve overall retrieval quality and interpretability. Finally, process knowledge-infused RAG utilizes domain-specific tools to reorder retrieved content based on validated workflows. Preliminary results from mental health risk assessment tasks indicate that this neurosymbolic approach enhances both transparency and overall performance.

Problem

• Retrieval Augmented Generation
• interpretability
• neurosymbolic
• transparency
• knowledge graph

Innovation

• Neurosymbolic RAG
• Knowledge Graph
• Interpretable Retrieval
• Modulation Networks
• Retrieval-Augmented Generation