Welcome to the tag category page for Convolution!
A Knowledge Graph is a data layer used for answering complex queries across data silos. It represents and organizes contextualized data in the form of graphs. Google's Knowledge Graph presents information about people, places, or things within knowledge panels. The Knowledge Graph API Search API is free for developers up to a quota of 100,000 read calls per day. A knowledge graph in NLP stores data resulting from an information extraction task in triples, consisting of a subject, a predicate, and an object. Knowledge graphs create supreme connectedness between data silos and are a flexible, reusable data layer.
A graph neural network (GNN) is a type of artificial neural network (ANN) designed to process and analyze data represented in graph form. GNNs operate on the entire graph structure, including nodes, edges, and global context, allowing them to preserve graph symmetries. They are a type of deep learning method capable of performing inference on data described by graphs, and can be applied to a wide range of domains. While similar to other neural network architectures, GNNs have unique features that set them apart, including the ability to process non-Euclidean structured data, as well as overcoming difficulties specific to processing graphs, such as vanishing gradients and overfitting. GNNs are also differentiated from other graph-based neural networks, such as graph convolutional networks (GCNs), by their use of shared weights in each recurrent step. Overall, GNNs represent a powerful tool for deep learning on complex and structured data.
