Investigative reporters have a challenging task of identifying complex networks of people, places, and events gleaned from a mixed collection of sources. Turning those various documents, electronic records, and research into a searchable and actionable collection of facts is an interesting and difficult technical challenge. Friedrich Lindenberg created the Aleph project to address this issue and in this episode he explains how it works, why he built it, and how it is being used. He also discusses his hopes for the future of the project and other ways that the system could be used.
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- Your host as usual is Tobias Macey and today I’m interviewing Friedrich Lindenberg about Aleph, a tool to perform entity extraction across documents and structured data
- How did you get introduced to Python?
- Can you start by explaining what Aleph is and how the project got started?
- What is investigative journalism?
- How does Aleph fit into their workflow?
- What are some other tools that would be used alongside Aleph?
- What are some ways that Aleph could be useful outside of investigative journalism?
- How is Aleph architected and how has it evolved since you first started working on it?
- What are the major components of Aleph?
- What are the types of documents and data formats that Aleph supports?
- Can you describe the steps involved in entity extraction?
- What are the most challenging aspects of identifying and resolving entities in the documents stored in Aleph?
- Can you describe the flow of data through the system from a document being uploaded through to it being displayed as part of a search query?
- What is involved in deploying and managing an installation of Aleph?
- What have been some of the most interesting or unexpected aspects of building Aleph?
- Are there any particularly noteworthy uses of Aleph that you are aware of?
- What are your plans for the future of Aleph?
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