Category: Graphs and Data Stores
On Semantic RAG. Q&A with Imran Chaudhri
Q1. Imran, one of the most compelling promises of Semantic RAG with Progressive Graphs is improved accuracy and reduced risk in AI systems. Can you walk us through how this approach delivers on these...
On Ontologies and AI. Q&A with Mattia Ferrini
Q1. You’ve spent two decades working on decision science systems, and ontologies play a critical role in both data management and AI. Can you discuss how you approach the generation and maintenance of ontologies...
On the 4 properties for the data layer of AI applications. Q&A with Andreas Kollegger
Q1. Emil Eifrem identifies ‘extracting structure from unstructured data‘ as a critical property for AI data layers. How does Neo4j’s approach to LLM-driven entity extraction and relationship discovery differ from traditional NLP pipelines? Can...
On Graph Databases, Gen AI and the Cloud. Q&A with Jim Webber
Q1. The demand for graph databases is being regarded as essential infrastructure for AI systems. Why? Large Language Models (LLMs) are incredibly powerful for AI systems, but businesses have to balance the models’ creativity...
On Data Infrastructure at LinkedIn. Q&A with Kartik Paramasivam
This is all real. Trillions of events are processed every day by stream processing applications at LinkedIn. Q1. You are VP – Data Infrastructure at LinkedIn. What are your responsibilities at LinkedIn? And what...
On Graph Databases. Q&A with Sudhir Hasbe
Q1. You recently joined Neo4j’s executive leadership team as Chief Product Officer (CPO). What are your responsibilities and expectations? I joined Neo4j in April 2023, and I am responsible for the product vision, strategy, and...
The Cost Benefits of Serverless Graph Databases. Q&A with Brad Bebee.
” We see customer usage of graph databases growing rapidly. Companies are using the relationships in their data to transform their businesses.” Q1. Since we last spoke about graph databases, what has changed? A1. Over...