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 ...

Graphs are everywhere. Whenever a delivery company models a road network, a power grid operator tracks the flow of ...

Graphs are everywhere. From technology to finance, they often model valuable information such as people, networks, biological pathways and more. Often, scientists and technologists need to come up ...

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 ...

Graph neural networks (GNNs) are a type of neural network architecture and deep learning method that can help users analyze graphs, enabling them to make predictions based on the data described by a ...

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. This eMag examines how architects can lead ...

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 ...

3D configurations of atoms dictate all materials properties. Quantitative predictions of accurate equilibrium structures, 3D coordinates of all atoms, from a chemical graph, a representation of the ...

Start working toward program admission and requirements right away. Work you complete in the non-credit experience will transfer to the for-credit experience when you ...