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Property graphs and RDF knowledge graphs are reaching a scale where processing them on a single machine is no longer practical, making graph partitioning an essential component for enabling distributed storage, querying, and analytics. Traditional partitioning methods, however, mainly focus on graph topology and often overlook the semantic information embedded in labels, entity types, schemas, and ontological constraints. This half-day tutorial explores semantic-aware partitioning of property graphs, focusing on approaches that leverage structural and semantic information to create partitions with improved query locality and reduced communication overhead across distributed systems. The tutorial first introduces the fundamentals of graph partitioning, including edge-cut, vertex-cut, and streaming-based methods, together with the main characteristics of property graphs and RDF knowledge graphs. It then discusses how semantic information can influence partitioning strategies and how these techniques can be integrated with modern distributed graph databases and query processing frameworks. Throughout the tutorial lectures, participants will be introduced to semantic-aware partitioning approaches for real-world knowledge graphs and will gain insights into their behavior compared with traditional semantics-agnostic techniques. The tutorial is aimed at researchers and practitioners in the Semantic Web and Knowledge Graph communities who are interested in scalable graph data management. No advanced background is required beyond basic familiarity with RDF or property graphs. By the end of the tutorial, participants will gain an overview of current research directions, practical experience with relevant tools, and insights into open challenges at the intersection of semantics and distributed graph processing.
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