# Truveta Mapper: A Zero-shot Ontology Alignment Framework

arXiv: https://arxiv.org/abs/2301.09767 · HTML: https://mlsanity.com/paper/2301.09767/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 36 references that are in the map.
- First posted on arXiv 24 Jan 2023.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions](https://mlsanity.com/paper/1905.10044/) (2019)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://mlsanity.com/paper/2105.13626/) (2021)
- [Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation](https://mlsanity.com/paper/1611.04558/) (2016)
- [OWL2Vec*: Embedding of OWL Ontologies](https://mlsanity.com/paper/2009.14654/) (2020)
- [BERTMap: A BERT-based Ontology Alignment System](https://mlsanity.com/paper/2112.02682/) (2021)
- [EDITOR: an Edit-Based Transformer with Repositioning for Neural Machine Translation with Soft Lexical Constraints](https://mlsanity.com/paper/2011.06868/) (2020)
- [Machine Learning-Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching](https://mlsanity.com/paper/2205.03447/) (2022)
- [Ontology Alignment in the Biomedical Domain Using Entity Definitions and Context](https://mlsanity.com/paper/1806.07976/) (2018)

## Built on by (most cited first, 5 of 5)

- [Exploring Large Language Models for Ontology Alignment](https://mlsanity.com/paper/2309.07172/) (2023)
- [LLMs4OM: Matching Ontologies with Large Language Models](https://mlsanity.com/paper/2404.10317/) (2024)
- [DeepOnto: A Python Package for Ontology Engineering with Deep Learning](https://mlsanity.com/paper/2307.03067/) (2023)
- [Language Models as Ontology Encoders](https://mlsanity.com/paper/2507.14334/) (2025)
- [MapperGPT: Large Language Models for Linking and Mapping Entities](https://mlsanity.com/paper/2310.03666/) (2023)

## Abstract (arXiv)

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.