arXiv AI By Cosimo Gregucci, Bo Xiong, Daniel Hernandez, Lorenzo Loconte, Pasquale Minervini, Steffen Staab, Antonio Vergari

Is Complex Query Answering Really Complex?

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arXiv Machine Learning
Sep 3

Oracle, will I ever learn? A study of prediction convergence and complementarity across link prediction models

The paper investigates how different link prediction models for knowledge graphs produce varying predictions and explores the extent of their complementary knowledge. By evaluating an oracle that selects the best prediction from a set of models, the authors show a significant performance gap between individual models and the oracle, indicating substantial complementarity. However, this complementarity quickly saturates as more models are added, leaving many queries unsolved even with many models.

By Guillaume M\'erou\'e, Fabien Gandon, Pierre Monnin
arXiv AI
Aug 11

KGCache: Amortized Subgraph Retrieval for KG Reasoning with LLMs

arXiv:2608. 07954v1 Announce Type: new Abstract: Large language models can answer knowledge-intensive questions more reliably when they are grounded with knowledge graphs, but systems such as Think-on-Graph and Reasoning-on-Graph repeatedly query the same graph neighborhoods across different questions.

By Uros Stanic, Changcheng Yuan, Sabuj Laskar, Ariful Azad