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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, and Paolo Atzeni

Published in: OASIcs, Volume 119, The Provenance of Elegance in Computation - Essays Dedicated to Val Tannen (2024)

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.

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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)

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@InProceedings{baldazzi_et_al:OASIcs.Tannen.1,
  author =	{Baldazzi, Teodoro and Bellomarini, Luigi and Ceri, Stefano and Colombo, Andrea and Gentili, Andrea and Sallinger, Emanuel and Atzeni, Paolo},
  title =	{{Explaining Enterprise Knowledge Graphs with Large Language Models and Ontological Reasoning}},
  booktitle =	{The Provenance of Elegance in Computation - Essays Dedicated to Val Tannen},
  pages =	{1:1--1:20},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-320-1},
  ISSN =	{2190-6807},
  year =	{2024},
  volume =	{119},
  editor =	{Amarilli, Antoine and Deutsch, Alin},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.Tannen.1},
  URN =		{urn:nbn:de:0030-drops-200971},
  doi =		{10.4230/OASIcs.Tannen.1},
  annote =	{Keywords: provenance, ontological reasoning, language models, knowledge graphs}
}

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DOI: 10.4230/DagSemProc.10042.4

Towards Expressive Stream Reasoning

Authors: Heiner Stuckenschmidt, Stefano Ceri, Emanuele Della Valle, and Frank van Harmelen

Published in: Dagstuhl Seminar Proceedings, Volume 10042, Semantic Challenges in Sensor Networks (2010)

Abstract

Stream Data processing has become a popular topic in database research addressing the challenge of efficiently answering queries over continuous data streams. Meanwhile data streams have become more and more important as a basis for higher level decision processes that require complex reasoning over data streams and rich background knowledge. In previous work the foundation for complex reasoning over streams and background knowledge was laid by introducing technologies for wrapping and querying streams in the RDF data format and by supporting simple forms of reasoning in terms of incremental view maintenance. In this paper, we discuss how this existing technologies should be extended toward richer forms of reasoning using Sensor Networks as a motivating example.

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Heiner Stuckenschmidt, Stefano Ceri, Emanuele Della Valle, and Frank van Harmelen. Towards Expressive Stream Reasoning. In Semantic Challenges in Sensor Networks. Dagstuhl Seminar Proceedings, Volume 10042, pp. 1-14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2010)

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@InProceedings{stuckenschmidt_et_al:DagSemProc.10042.4,
  author =	{Stuckenschmidt, Heiner and Ceri, Stefano and Della Valle, Emanuele and van Harmelen, Frank},
  title =	{{Towards Expressive Stream Reasoning}},
  booktitle =	{Semantic Challenges in Sensor Networks},
  pages =	{1--14},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2010},
  volume =	{10042},
  editor =	{Karl Aberer and Avigdor Gal and Manfred Hauswirth and Kai-Uwe Sattler and Amit P. Sheth},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.10042.4},
  URN =		{urn:nbn:de:0030-drops-25555},
  doi =		{10.4230/DagSemProc.10042.4},
  annote =	{Keywords: Streaming Data, Reasoning, C-SPARQL, Sensor Networks}
}

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Documents authored by Ceri, Stefano

Explaining Enterprise Knowledge Graphs with Large Language Models and Ontological Reasoning

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Towards Expressive Stream Reasoning

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