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  <title>Melliyal Annamalai Talks Knowledge Graphs</title>
  <description>Melliyal Annamalai explains why knowledge graphs are becoming a critical foundation for modern AI by providing the context that helps AI agents deliver more accurate and trustworthy results. The conversation explores how property graphs, RDF graphs, vectors, and graph analytics work together to strengthen enterprise AI applications.  In this episode of&amp;amp;nbsp;Digital Impact Radio Series 9,&amp;amp;nbsp;Franco Ucci&amp;amp;nbsp;speaks with Melliyal Annamalai about the growing importance of knowledge graphs in the era of artificial intelligence. As AI systems become more capable, the discussion explores why knowledge graphs are moving beyond their traditional role in analytics to become an essential source of context for AI agents, retrieval systems, and enterprise applications.  Melliyal explains the differences between property graphs and RDF graphs, also known as semantic or knowledge graphs, and describes how each graph model supports different use cases. The conversation examines how knowledge graphs capture business concepts, relationships, and domain expertise in a structured, standards-based way that helps AI systems produce more precise, explainable, and trustworthy outcomes. Real-world examples from fraud detection, financial services, supply chain analysis, and enterprise metadata illustrate how graph technologies continue to evolve alongside AI.  Franco and Melliyal also discuss how vectors and graphs complement one another, combining semantic similarity with explicit relationships to provide richer context for AI. The episode concludes with practical insights into industry ontologies, graph standards, Oracle Graph, and the growing ecosystem of tools and learning resources that are making knowledge graphs increasingly accessible for enterprise AI solutions. </description>
  <author_name>Digital Impact Radio</author_name>
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