Rosario Napoli, Antonio Celesti, Massimo Villari, Maria Fazio
原文摘要
Knowledge Graphs (KGs) and Large Language Models (LLMs) represent two approaches for knowledge representation and reasoning over heterogeneous data. Recent research has explored their integration through Graph Retrieval-Augmented Generation (GraphRAG) systems, which use KGs to provide structured context to LLMs. However, existing GraphRAG approaches treat KGs as homogeneous graphs and rely on textual similarity between the input prompt and the context, rather than using it as a source of deterministic knowledge. This work proposes an agent-based GraphRAG system that introduces a symbolic layer to integrate deterministic reasoning alongside text similarities. This approach is based on the definition of typed centralities for KGs, where centrality measures are computed separately for each specific KG's class. This information is used to construct a symbolic representation that is provided to the LLM as a deterministic source. The proposed system was mathematically validated and evaluated on a real-world POLE (Person, Object, Location, Event) dataset, comparing reasoning and information retrieval with and without the symbolic layer. The results indicate that the our approach leads to responses that are more consistent with the context, improving the generated outputs.
Rosario Napoli, Mark Adrian Gambito, Antonio Celesti, Maria Fazio
原文摘要
In modern Big Data architectures, scalability is often addressed through infrastructural expansion, involving vertical or horizontal scaling to manage growing data volumes. However, this hardware-centric perspective overlooks the logical scalability of the storage layer. A central challenge lies in how data modeling choices affect the storage layer of big data architectures. Specifically, when the data model is misaligned with the underlying query patterns, performance bottlenecks emerge and persist regardless of the available computational resources. This paper investigates the concept of logical scalability by conducting a comparative performance analysis of Document, Graph and Multi-Model database paradigms under varying workloads, queries and dataset scales. Our results demonstrate that performance degradation caused by model misalignment cannot be mitigated by additional computational resources alone. Increasing the number of machines while retaining a structurally inadequate data model yields performance equivalent to or worse than a single-machine configuration, as distributed coordination overhead compounds the inefficiencies of the underlying logical model. We demonstrate that efficiency is maximized when the logical structure of the database aligns with the inherent nature of the data and the intended analysis scope, a condition that proves to be more decisive than any infrastructural scaling strategy.