Boosting RAG Application Precision with Knowledge Graph Integration

Boosting RAG Application Precision with Knowledge Graph Integration

24 May 202413:05pm - 24 May 202414:05pm

Boosting RAG Application Precision with Knowledge Graph Integration

About the Event

RAG Models and Their Challenges

Retrieval-Augmented Generation (RAG) models are revolutionizing natural language processing by combining retrieval-based techniques with advanced generative models. These models fetch pertinent information from extensive datasets and generate responses that are not only precise but also contextually enriched, setting new standards for AI in conversational, question-answering, and interactive applications. Despite their promise, RAG models face several challenges, such as ensuring the contextual relevance and timeliness of retrieved data, managing complex queries that demand a deep understanding of relationships and contexts, and integrating both structured and unstructured data for comprehensive responses.

 

Leveraging Knowledge Graphs to Enhance RAG Models

Knowledge Graphs, structured representations of knowledge where entities (nodes) are connected by relationships (edges), can address these challenges effectively. They offer a dynamic way to catalog information along with its context and interrelations. For instance, in a medical knowledge graph, nodes like symptoms, diseases, and treatments are interconnected by relationships such as symptoms of or treated by, providing a rich contextual background. Additionally, embeddings, which convert text into high-dimensional vectors to capture semantic meaning, enhance search capabilities within Knowledge Graphs, ensuring that retrieved information aligns closely with the query’s context, further benefiting RAG models.

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Who is this DataHour for?

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About the Speaker

Arun Narayanan

Arun Narayanan

Lead Data Scientist at American Express

Arun is a storytelling Data Scientist, with a quest for learning and exploring technology and statistics. He has over 10 plus years of experience in AI & ML software design and development across Pharma, Banking and Retail domains, currently working as Lead Data Scientist at American Express.

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