Entity-Resolved Knowledge Graphs

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Session Track:

Session Time: 09:30 pm - 10:00 pm November 7

Session description

Entity resolution (ER) is a complex process focused on data quality for knowledge graph construction and updates, with crucial impact on the quality and trust of downstream AI apps. This talk shows how to use ER with open data to construct a KG in Neo4j, then used in GraphRAG based on LlamaIndex. We'll focus on linking multiple datasets (beneficial ownership, sanctions, GLEIF, etc.) regarding corporates in the London metro area, then explore hidden relations through graph visualization and chat interaction. This example illustrates KG work used in production to investigate _ultimate beneficial owner_ (UBO) and sanctions compliance.

Speaker

Paco Nathan

Principal DevRel Engineer, Senzing.com

Paco Nathan leads DevRel for the Entity Resolved Knowledge Graph practice area at Senzing.com and is a computer scientist with +40 years of tech industry experience and core expertise in data science, natural language, graph technologies, and cloud computing. He's the author of numerous books, videos, and tutorials about these topics. Paco advises Argilla.io (acq. Hugging Face), Kurve.ai, KungFu.ai, and DataSpartan, and is lead committer for the pytextrank and kglab open source projects. Formerly: Director of Learning Group at O'Reilly Media; and Director of Community Evangelism at Databricks.