Building A Knowledge Graph Of Commercial Real Estate At Cherre

Episode • Apr 7, 2020 • 45m

Summary

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. In this episode John Maiden talks about how Cherre builds knowledge graphs that provide powerful insights for their customers and the engineering challenges of building a scalable graph. If you’re wondering how to extract additional business value from existing data, this episode will provide a way to expand your data resources.

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Your host is Tobias Macey and today I’m interviewing John Maiden about how Cherre is building and using a knowledge graph of commercial real estate information

Interview

Introduction
How did you get involved in the area of data management?
Can you start by describing what Cherre is and the role that data plays in the business?
What are the benefits of a knowledge graph for making real estate investment decisions?
What are the main ways that you and your customers are using the knowledge graph?
- What are some of the challenges that you face in providing a usable interface for end-users to query the graph?
What technology are you using for storing and processing the graph?
- What challenges do you face in scaling the complexity and analysis of the graph?
What are the main sources of data for the knowledge graph?
What are some of the ways that messiness manifests in the data that you are using to populate the graph?
- How are you managing cleaning of the data and how do you identify and process records that can’t be coerced into the desired structure?
- How do you handle missing attributes or extra attributes in a given record?
How did you approach the process of determining an effective taxonomy for records in the graph?
What is involved in performing entity extraction on your data?
What are some of the most interesting or unexpected questions that you have been able to ask and answer with the graph?
What are some of the most interesting/unexpected/challenging lessons that you have learned in the process of working with this data?
What are some of the near and medium term improvements that you have planned for your knowledge graph?
What advice do you have for anyone who is interested in building a knowledge graph of their own?

Contact Info

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
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The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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