Exploring Large Language Models for Ontology Alignment
This work investigates the applicability of recent generative Large Language Models (LLMs), such as the GPT series and Flan-T5, to ontology alignment for identifying concept equivalence mappings across ontologies. To test the zero-shot performance of Flan-T5-XXL and GPT-3.5-turbo, we leverage challenging subsets from two equivalence matching datasets of the OAEI Bio-ML track, taking into account concept labels and structural contexts. Preliminary findings suggest that LLMs have the potential to outperform existing ontology alignment systems like BERTMap, given careful framework and prompt design.
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Methods
Adam •
Attention Dropout •
BPE •
Cosine Annealing •
Dense Connections •
Discriminative Fine-Tuning •
Dropout •
Fixed Factorized Attention •
Flan-T5 •
GELU •
GPT •
GPT-3 •
Layer Normalization •
Linear Layer •
Linear Warmup With Cosine Annealing •
Multi-Head Attention •
Ontology •
Residual Connection •
Scaled Dot-Product Attention •
Softmax •
Strided Attention •
Weight Decay
