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Multi-Layered Legislative Knowledge Management with Property Graphs

Authors: Andrea Colombo, Francesco Cambria, and Francesco Invernici

Published in: TGDK, Volume 4, Issue 2 (2026): Special Issue on Data Management for (Knowledge) Graphs. Transactions on Graph Data and Knowledge, Volume 4, Issue 2

Abstract

The sparse nature and intricate set of relationships between legislative acts pose a significant challenge in the choice of the underlying database model, which both allows for performing structured queries and developing intuitive and smooth knowledge management. In this paper, we propose to use Property Graphs as a powerful alternative for managing legislative knowledge. First, we discuss how graph queries are a valid alternative solution to standard legislative knowledge management by showing how our data model fully captures the problem of law versioning (i.e., the existence of many versions for the same law). Then, we analyze, propose and implement innovative ways for monitoring the legislative system using Property Graph tools that have been recently standardized and developed, such as triggers and graph-based association rules, which empower our model of advanced ways of handling legislative data. For instance, we will show how we can use these tools to develop intelligent warning systems that inform stakeholders of critical changes in legislation through active rule reasoning or to detect shifts in graph patterns via continuous monitoring of significant association patterns. Lastly, we will present an approach to expand the Property Graph model to also include laws from an institutional lower layer: regional governments. We then show how this would open new ways to exploit Property Graph tools for legislative knowledge management.

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Andrea Colombo, Francesco Cambria, and Francesco Invernici. Multi-Layered Legislative Knowledge Management with Property Graphs. In Special Issue on Data Management for (Knowledge) Graphs. Transactions on Graph Data and Knowledge (TGDK), Volume 4, Issue 2, pp. 4:1-4:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@Article{colombo_et_al:TGDK.4.2.4,
  author =	{Colombo, Andrea and Cambria, Francesco and Invernici, Francesco},
  title =	{{Multi-Layered Legislative Knowledge Management with Property Graphs}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{4:1--4:23},
  ISSN =	{2942-7517},
  year =	{2026},
  volume =	{4},
  number =	{2},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.4.2.4},
  URN =		{urn:nbn:de:0030-drops-275856},
  doi =		{10.4230/TGDK.4.2.4},
  annote =	{Keywords: data management, property graphs, triggers, law}
}

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DOI: 10.4230/OASIcs.Tannen.1

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.

Cite as

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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Documents authored by Colombo, Andrea

Multi-Layered Legislative Knowledge Management with Property Graphs

Abstract

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Explaining Enterprise Knowledge Graphs with Large Language Models and Ontological Reasoning

Abstract

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