6 citations · 10 across the 10 of their papers we have counts for
4 papers · 1 filter
Mind the Data Gap: Bridging LLMs to Enterprise Data Integration
Moe Kayali, Fabian Wenz, Nesime Tatbul +1
Leading large language models (LLMs) are trained on public data. However, most of the world's data is dark data that is not publicly accessible, mainly in the form of private organ…
QirK: Question Answering via Intermediate Representation on Knowledge Graphs
Jan Luca Scheerer, Anton Lykov, Moe Kayali +4
We demonstrate QirK, a system for answering natural language questions on Knowledge Graphs (KG). QirK can answer structurally complex questions that are still beyond the reach of e…
Making LLMs Work for Enterprise Data Tasks
Çağatay Demiralp, Fabian Wenz, Peter Baile Chen +3
Large language models (LLMs) know little about enterprise database tables in the private data ecosystem, which substantially differ from web text in structure and content. As LLMs'…
Color: A Framework for Applying Graph Coloring to Subgraph Cardinality Estimation
Kyle Deeds, Diandre Sabale, Moe Kayali +1
Graph workloads pose a particularly challenging problem for query optimizers. They typically feature large queries made up of entirely many-to-many joins with complex correlations.…