Equal contribution; order determined alphabetically.

Fully connected Graph Transformers (GT) have rapidly become prominent in the static graph community as an alternative to Message-Passing models, which suffer from a lack of expressivity, oversquashing, and under-reaching.

For instance, researchers are examining the gene network derived from the gene expression data of patients to investigate a rare disease.

However, it is nontrivial to directly apply LLMs for EA since the annotation space in real-world KGs is large. LLMs could also generate noisy labels that may mislead the alignment.

Typicalness-A ware Learning (T AL) to address this issue and improve failure detection performance.

In this work, we propose a novel calibration method that can be used to combat hallucinations.