  In this paper, a new perspective is suggested for unsupervised Ontology
Matching (OM) or Ontology Alignment (OA) by treating it as a translation task.
Ontologies are represented as graphs, and the translation is performed from a
node in the source ontology graph to a path in the target ontology graph. The
proposed framework, Truveta Mapper (TM), leverages a multi-task
sequence-to-sequence transformer model to perform alignment across multiple
ontologies in a zero-shot, unified and end-to-end manner. Multi-tasking enables
the model to implicitly learn the relationship between different ontologies via
transfer-learning without requiring any explicit cross-ontology manually
labeled data. This also enables the formulated framework to outperform existing
solutions for both runtime latency and alignment quality. The model is
pre-trained and fine-tuned only on publicly available text corpus and
inner-ontologies data. The proposed solution outperforms state-of-the-art
approaches, Edit-Similarity, LogMap, AML, BERTMap, and the recently presented
new OM frameworks in Ontology Alignment Evaluation Initiative (OAEI22), offers
log-linear complexity, and overall makes the OM task efficient and more
straightforward without much post-processing involving mapping extension or
mapping repair. We are open sourcing our solution.
