Senzing is the first to deliver real-time AI for entity resolution. Senzing entity resolution
provides organizations with new insights to make better business decisions while reducing
operational costs. The Senzing API is the fastest and easiest way for commercial software
vendors, and organizations in financial services, public sector, information services and other industries to integrate highly accurate, flexible and scalable entity resolution capabilities into applications, data fabrics and other data services. Senzing is Smarter Entity Resolution. 

Senzing

# **Resources**

Get the slides [here](https://www.dropbox.com/scl/fo/a1rqe5kbnnkh27ncv5bje/AGI6fIwbZmoN70Z-213TQXM?rlkey=qz4z5rh2z1mu0ltnef5e7lkh0\&st=ik3gxuf9\&dl=0)

Visit Dr. Clair Sullivan's Github [here](https://github.com/cj2001)

Read the Neo4 blog featuring: SafeGraph + WHISARD + PPP [here](https://neo4j.com/developer-blog/entity-resolved-knowledge-graphs/)

Read the guest blog for Senzing [here](https://senzing.com/knowledge-graphs-graph-rag/)

Dr. Clair Sullivan's tutorial [here](https://linkedin.com/pulse/when-graphrag-goesbad-study-why-you-cannot-afford-ignore-sullivan-7ymnc/)

[NEo4j Graph Builder](https://llm-graph-builder.neo4jlabs.com/)

[Retrieval-Augmented Generation for AI-Generated Content: A Survey](https://arxiv.org/abs/2402.19473)

[A Benchmark To Understand The Role Of Knowledge Graphs On Large Language Model’s Accuracy For Question Answering On Enterprise SQL Databases](https://arxiv.org/pdf/2311.07509)

**Graphs are having a moment!**

Thanks to the GenAI revolution, more people than ever are turning to graphs to solve problems like retrieval-augmented generation (RAG) and minimize hallucinations common in GenAI models. Graph structures excel at representing complex, interconnected data—critical for improving the contextual understanding that generative AI requires. By leveraging graphs, AI systems can better discover relationships, link entities, and enrich data, making them more effective at handling real-world complexity. As AI evolves, graphs are emerging as key enablers for more sophisticated, context-aware insights.

**However, applying graphs to back GenAI applications is not without challenges.** Extracting nodes and relationships from unstructured text via natural language processing (NLP) is difficult, as language is rich in context, idiomatic expressions, technical jargon, and implicit meanings that don’t neatly fit into graph structures. While large language models (LLMs) reflect general language usage, they often fail to capture subtle nuances and technical details, leading to oversimplified or inaccurate graph representations. This disconnect between the richness of language and the structured nature of graphs can hinder a system’s ability to generate significant insights.

**In this talk**, we will explore the use of graphs "from the trenches," examining real-world challenges encountered when applying them in complex systems. While graphs offer immense theoretical potential, translating that into practical applications often reveals unforeseen difficulties. From handling incomplete, ambiguous, or unresolved data to scaling large, interconnected datasets, the journey is rarely straightforward. We'll also discuss the challenges of integrating graphs with AI models, where the structured nature of graphs can clash with the messiness of real-world information. These practical insights will highlight the gap between the idealized use of graphs and the realities faced in the field.
