You’ve set your sights on a collection of prime off-the-market New York City properties whose value is likely to double in the next couple of years. With your excellent sales skills, you’re confident you can get the owners to sell at a fair price, if you can determine who the owners are. You have a knowledge graph—built from tax records, property deeds, and permits—that identifies entities associated with the properties. These entities might not be the true owners, but identifying them could help you determine who the true owners are.
Your task is to transform the knowledge graph into a representation that can be processed by a machine learning model later. Using simple graph and NLP techniques, including node2vec—one of the most influential algorithms in the graph community—you’ll improve the quality of the data by removing the noise from the knowledge graph. You’ll convert the nodes in your graph into embeddings, analyze how well your embeddings represent the underlying knowledge graph, and develop insights on tuning the node2vec hyperparameters. When you’re done, you’ll have learned techniques for analyzing and visualizing embeddings and associating them to the original graph, helping you determine who the “hidden” owners are.
This liveProject is for data scientists who have a background in graph theory and machine learning and are interested in applying these techniques to a knowledge graph. To begin these liveProjects, you’ll need to be familiar with the following:TOOLS
In this liveProject, you’ll learn techniques for analyzing and visualizing graph embeddings and associating them back to the original graph, and you’ll understand how different schemas can impact the knowledge representation.
geekle is based on a wordle clone.