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Explaining Enterprise Knowledge Graphs with Large Language Models and Ontological Reasoning

Authors Teodoro Baldazzi , Luigi Bellomarini , Stefano Ceri , Andrea Colombo , Andrea Gentili , Emanuel Sallinger , Paolo Atzeni

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Author Details

Teodoro Baldazzi
  • Università Roma Tre, Italy
Luigi Bellomarini
  • Banca d'Italia, Roma, Italy
Stefano Ceri
  • Politecnico di Milano, Italy
Andrea Colombo
  • Politecnico di Milano, Italy
Andrea Gentili
  • Banca d'Italia, Roma, Italy
Emanuel Sallinger
  • TU Wien, Austria
  • University of Oxford, UK
Paolo Atzeni
  • Università Roma Tre, Italy

Funding

The work on this paper was partially supported by the Vienna Science and Technology Fund (WWTF) [10.47379/ICT2201, 10.47379/VRG18013, 10.47379/NXT22018]; and the Christian Doppler Research Association (CDG) JRC LIVE.
  • Ceri, Stefano: S.C. is supported by the PNRR-PE-AI FAIR project, funded by the NextGenerationEU program.
  • Colombo, Andrea: A.C. kindly acknowledges INPS for the funding of his Ph.D. program.

Cite AsGet BibTex

Teodoro Baldazzi, Luigi Bellomarini, Stefano Ceri, Andrea Colombo, Andrea Gentili, Emanuel Sallinger, and Paolo Atzeni. Explaining Enterprise Knowledge Graphs with Large Language Models and Ontological Reasoning. In The Provenance of Elegance in Computation - Essays Dedicated to Val Tannen. Open Access Series in Informatics (OASIcs), Volume 119, pp. 1:1-1:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/OASIcs.Tannen.1

Abstract

In recent times, the demand for transparency and accountability in AI-driven decisions has intensified, particularly in high-stakes domains like finance and bio-medicine. This focus on the provenance of AI-generated conclusions underscores the need for decision-making processes that are not only transparent but also readily interpretable by humans, to built trust of both users and stakeholders. In this context, the integration of state-of-the-art Large Language Models (LLMs) with logic-oriented Enterprise Knowledge Graphs (EKGs) and the broader scope of Knowledge Representation and Reasoning (KRR) methodologies is currently at the cutting edge of industrial and academic research across numerous data-intensive areas. Indeed, such a synergy is paramount as LLMs bring a layer of adaptability and human-centric understanding that complements the structured insights of EKGs. Conversely, the central role of ontological reasoning is to capture the domain knowledge, accurately handling complex tasks over a given realm of interest, and to infuse the process with transparency and a clear provenance-based explanation of the conclusions drawn, addressing the fundamental challenge of LLMs' inherent opacity and fostering trust and accountability in AI applications. In this paper, we propose a novel neuro-symbolic framework that leverages the underpinnings of provenance in ontological reasoning to enhance state-of-the-art LLMs with domain awareness and explainability, enabling them to act as natural language interfaces to EKGs.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Knowledge representation and reasoning
  • Computing methodologies → Natural language processing
  • Theory of computation → Data provenance
Keywords
  • provenance
  • ontological reasoning
  • language models
  • knowledge graphs

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