CTO, Cherre Inc.
Ron Bekkerman is the CTO of Cherre Inc., an AI-powered real estate data integration platform. From 2013 to 2018, Ron was Assistant Professor and Director of the Big Data Science Lab at the University of Haifa, Israel. Prior to that, he was the Chief Data Officer of Viola Ventures, a founding member of the Data Science team at LinkedIn, and a Research Scientist at HP Labs in the Bay Area. He received his B.Sc. and M.Sc. in Computer Science from the Technion – Israel Institute of Technology, and his Ph.D. in Machine Learning from the University of Massachusetts, Amherst.
2020 Talk: Modeling Real Estate Ecosystem with Cherre's Knowledge Graph
Cherre’s knowledge graph is a model of the entire US real estate ecosystem. The graph incorporates hundreds of millions of entities such as properties, addresses, individual and commercial owners, lenders, brokers, estate managers, lawyers etc. as nodes – while the edges are various types of connections between the entities. A wealth of attributes are associated with each entity. Cherre’s knowledge graph is a closed-world graph: it allows inferring an absence of connection between two entities if there is no edge between them in the graph. Furthermore, Cherre’s graph is temporal: edges and nodes are being added and deleted on a timely basis. Some of the main challenges in constructing a closed-world graph from noisy data sources are entity resolution and disambiguation. In this talk, we will present parallel algorithms for entity resolution and disambiguation in Cherre’s knowledge graph, and outline our current work on assessing entity similarities using (temporal) node embedding.