Collaborative Research: III: Small: Towards Well-Rounded Graph Retrieval for Retrieval-Aug — NSF Award to University of Oregon Eug
Solutions to real-world problems, such as scientific document question-answering, cybersecurity diagnosis, and e-commerce personalization, can often be improved by augmenting the underlying generative artificial intelligence-based (Gen-AI) systems with retrieved external knowledge. Much of this external knowledge is or
| Award title | Collaborative Research: III: Small: Towards Well-Rounded Graph Retrieval for Retrieval-Aug |
|---|---|
| Award ID | 2524379 |
| Awardee | University of Oregon Eugene |
| City | EUGENE |
| State | OR |
| Amount obligated | $199,997 |
| Principal investigator | Yu Wang |
| Program | Info Integration & Informatics |
| Start date | 08/01/2025 |
| Abstract | Solutions to real-world problems, such as scientific document question-answering, cybersecurity diagnosis, and e-commerce personalization, can often be improved by augmenting the underlying generative artificial intelligence-based (Gen-AI) systems with retrieved external knowledge. Much of this external knowledge is organized in graph-structured formats that encode unique relational signals. For example, citation links among scientific papers reveal their deep intellectual dependencies across di |
| Source | NSF Awards |
$799/mo
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