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        <identifier>oai:drops-oai.dagstuhl.de:27585</identifier>
        <datestamp>2026-09-03T08:33:48Z</datestamp>
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          <dc:title>Multi-Layered Legislative Knowledge Management with Property Graphs</dc:title>
          <dc:creator>Colombo, Andrea</dc:creator>
          <dc:creator>Cambria, Francesco</dc:creator>
          <dc:creator>Invernici, Francesco</dc:creator>
          <dc:subject>data management</dc:subject>
          <dc:subject>property graphs</dc:subject>
          <dc:subject>triggers</dc:subject>
          <dc:subject>law</dc:subject>
          <dc:description>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.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Andrea Colombo and Francesco Cambria and Francesco Invernici</dc:contributor>
          <dc:date>2026</dc:date>
          <dc:relation>Is Part Of TGDK, Volume 4, Issue 2 (2024): Special Issue on Data Management for (Knowledge) Graphs. Transactions on Graph Data and Knowledge, Volume 4, Issue 2</dc:relation>
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          <dc:identifier>doi:10.4230/TGDK.4.2.4</dc:identifier>
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          <dc:language>eng</dc:language>
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