# Machine Learning-Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching

arXiv: https://arxiv.org/abs/2205.03447 · HTML: https://mlsanity.com/paper/2205.03447/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 33 references that are in the map.
- First posted on arXiv 6 May 2022.

## Builds on (most cited first, 7 of 7)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [Knowledge Graph Embedding for Link Prediction: A Comparative Analysis](https://mlsanity.com/paper/2002.00819/) (2020)
- [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)
- [Contextual Semantic Embeddings for Ontology Subsumption Prediction](https://mlsanity.com/paper/2202.09791/) (2022)
- [VeeAlign: Multifaceted Context Representation using Dual Attention for Ontology Alignment](https://mlsanity.com/paper/2102.04081/) (2021)

## Built on by (most cited first, 22 of 22)

- [Large Language Models and Knowledge Graphs: Opportunities and Challenges](https://mlsanity.com/paper/2308.06374/) (2023)
- [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)
- [A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises](https://mlsanity.com/paper/2306.04802/) (2023)
- [Ontology Embedding: A Survey of Methods, Applications and Resources](https://mlsanity.com/paper/2406.10964/) (2024)
- [Exploring the In-context Learning Ability of Large Language Model for Biomedical Concept Linking](https://mlsanity.com/paper/2307.01137/) (2023)
- [Knowledge Graphs for the Life Sciences: Recent Developments, Challenges and Opportunities](https://mlsanity.com/paper/2309.17255/) (2023)
- [OntoAligner: A Comprehensive Modular and Robust Python Toolkit for Ontology Alignment](https://mlsanity.com/paper/2503.21902/) (2025)
- [Reveal the Unknown: Out-of-Knowledge-Base Mention Discovery with Entity Linking](https://mlsanity.com/paper/2302.07189/) (2023)
- [Truveta Mapper: A Zero-shot Ontology Alignment Framework](https://mlsanity.com/paper/2301.09767/) (2023)
- [Ontology Matching with Large Language Models and Prioritized Depth-First Search](https://mlsanity.com/paper/2501.11441/) (2025)
- [Large Language Models as Oracles for Ontology Alignment](https://mlsanity.com/paper/2508.08500/) (2025)
- [Contextualized Structural Self-supervised Learning for Ontology Matching](https://mlsanity.com/paper/2310.03840/) (2023)
- [GenOM: Ontology Matching with Description Generation and Large Language Model](https://mlsanity.com/paper/2508.10703/) (2025)
- [Navigating Healthcare Insights: A Birds Eye View of Explainability with Knowledge Graphs](https://mlsanity.com/paper/2309.16593/) (2023)
- [OWL2Vec4OA: Tailoring Knowledge Graph Embeddings for Ontology Alignment](https://mlsanity.com/paper/2408.06310/) (2024)
- [OntoAligner Meets Knowledge Graph Embedding Aligners](https://mlsanity.com/paper/2509.26417/) (2025)
- [What can knowledge graph alignment gain with Neuro-Symbolic learning approaches?](https://mlsanity.com/paper/2310.07417/) (2023)
- [AgentMap: Joint Equivalence and Subsumption Discovery for Ontology Matching](https://mlsanity.com/paper/2607.27130/) (2026)
- [Improving Interoperability among Defence and National Security Ontologies: Analysis and Evaluation Tasks](https://mlsanity.com/paper/2608.05867/) (2026)
- [Sparse Autoencoders Are a Shared Coordinate System for Symbolic Matching](https://mlsanity.com/paper/2610.19636/) (2026)

## Abstract (arXiv)

Ontology Matching (OM) plays an important role in many domains such as bioinformatics and the Semantic Web, and its research is becoming increasingly popular, especially with the application of machine learning (ML) techniques. Although the Ontology Alignment Evaluation Initiative (OAEI) represents an impressive effort for the systematic evaluation of OM systems, it still suffers from several limitations including limited evaluation of subsumption mappings, suboptimal reference mappings, and limited support for the evaluation of ML-based systems. To tackle these limitations, we introduce five new biomedical OM tasks involving ontologies extracted from Mondo and UMLS. Each task includes both equivalence and subsumption matching; the quality of reference mappings is ensured by human curation, ontology pruning, etc.; and a comprehensive evaluation framework is proposed to measure OM performance from various perspectives for both ML-based and non-ML-based OM systems. We report evaluation results for OM systems of different types to demonstrate the usage of these resources, all of which are publicly available as part of the new BioML track at OAEI 2022.