Real-world data is far from perfect. It often contains multiple records belonging to the same entity (e.g., customer, property, etc.). These records can come from multiple systems and have variations across different attributes. This makes it hard to combine them together, especially with growing data volumes. Unfortunately, unharmonized data is not fit for use in customer analytics, risk and compliance and data engineers and scientists end up building some sort of rule or heuristic based system to manage it. This talk will cover Entity Resolution, which is also refered to as identity resolution, record linkage, deduplication or fuzzy matching. Entity Resolution helps to link and unify records that refer to the same real-world entity like customer or supplier. This talk will cover the needs and challenges of entity resolution, and introduce open source python package Zingg(https://github.com/zinggAI/zingg) which can be used to resolve entities at scale. We will discuss Zingg algorithms and Python API usage.
Real-world data is far from perfect. It often contains multiple records belonging to the same entity (e.g., customer, property, etc.). These records can come from multiple systems and have variations...
Unifying large language models (LLMs) and knowledge graphs (KGs) can address the shortcomings of LLMs such as lack of factual knowledge, hallucinations and lack of interpretability. Integrating LLMs with knowledge...
Clinical data summarization using generative AI involves leveraging advanced algorithms to extract, analyze, and condense vast amounts of medical information into concise, actionable insights. This technology employs natural language processing...
In this session, Leann Chen will introduce GraphRAG, a method that integrates knowledge graphs with large language models (LLMs) to enhance Retrieval-Augmented Generation (RAG) systems. Graph RAG can address challenges...
Building an AI prototype is easy and quick these days. Building production-grade systems is a different story. How do you keep moving quickly and run robustly? In this talk, we...
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