Knowledge Conceptualization Impacts RAG Efficacy

TL;DR

This paper investigates how different ways of conceptualizing and representing knowledge affect the performance of Agentic Retrieval-Augmented Generation systems, especially in their ability to effectively query knowledge sources, with implications for designing more interpretable and adaptable AI.

Contribution

It systematically evaluates the impact of knowledge structure and complexity on the efficacy of neurosymbolic AI systems in knowledge querying tasks.

Findings

Knowledge representation influences query effectiveness.

Structured knowledge improves interpretability.

Complexity impacts system adaptability.

Abstract

Explainability and interpretability are cornerstones of frontier and next-generation artificial intelligence (AI) systems. This is especially true in recent systems, such as large language models (LLMs), and more broadly, generative AI. On the other hand, adaptability to new domains, contexts, or scenarios is also an important aspect for a successful system. As such, we are particularly interested in how we can merge these two efforts, that is, investigating the design of transferable and interpretable neurosymbolic AI systems. Specifically, we focus on a class of systems referred to as ''Agentic Retrieval-Augmented Generation'' systems, which actively select, interpret, and query knowledge sources in response to natural language prompts. In this paper, we systematically evaluate how different conceptualizations and representations of knowledge, particularly the structure and complexity, impact an AI agent (in this case, an LLM) in effectively querying a triplestore. We report our results, which show that there are impacts from both approaches, and we discuss their impact and implications.