Starting from setting a product goal and going all the way to putting results in front of live customers, we talk about Cherre’s efforts to build a knowledge graph using Commercial Real Estate (CRE) data. Knowledge graphs are a data resource that can answer questions beyond the scope of traditional data analytics. By organizing and storing data to emphasize the relationship between entities, we can discover the complex connections between multiple sources of information. The CRE knowledge graph is a temporal graph (edges contain current and historical connections that can span decades) that connects entities such as properties, addresses, people, and companies to each other. Building such a graph is complicated by the inherently “messy” nature of CRE data, which we’ve collected from various data sources and requires NLP to standardize.
Our CRE graph is built to be the foundation of our ML efforts, allowing us to solve complex problems that leverage the connected nature of the data. This talk discusses the combination of multiple disciplines (NLP, data mining, graph analytics, deep learning) and domain knowledge that we use to build and extract value from the graph. Since this is done at the scale of big data (hundreds of millions of nodes, billions of edges), creating smart metrics to measure success is also discussed.