Existing graph-based retrieval-augmented generation (RAG) systems represent knowledge with binary relations and rely primarily on semantic similarity for retrieval. This design struggles with ...

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Understand the principles of efficient algorithms for dealing with large scale data sets and be able to select appropriate algorithms for specific problems. Understand and be able to apply the main ...

Graph technology has become a requirement for the modern enterprise. Companies in virtually every industry, from healthcare to energy to financial services, are applying the power of graph analytics ...

AI, natural language processing and semantic search applications are a driving force behind emerging data management operations. Data teams can use knowledge graphs in conjunction with these tools to ...

Machine learning, task automation and robotics are already widely used in business. These and other AI technologies are about to multiply, and we look at how organizations can best take advantage of ...

Researchers have developed IGA, an unsupervised graph alignment framework that uses isomorphism-aware neural networks to ...

Many organizations use data fabrics to connect disparate data sources to a central access point, regardless of their type or location. Some take this further by incorporating knowledge graphs into ...