# OPA2Vec: combining formal and informal content of biomedical ontologies to improve similarity-based prediction

arXiv: https://arxiv.org/abs/1804.10922 · HTML: https://mlsanity.com/paper/1804.10922/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2018.

## Built on by (most cited first, 25 of 25)

- [OntoProtein: Protein Pretraining With Gene Ontology Embedding](https://mlsanity.com/paper/2201.11147/) (2022)
- [Contextual Semantic Embeddings for Ontology Subsumption Prediction](https://mlsanity.com/paper/2202.09791/) (2022)
- [Faithiful Embeddings for EL++ Knowledge Bases](https://mlsanity.com/paper/2201.09919/) (2022)
- [Dual Box Embeddings for the Description Logic EL++](https://mlsanity.com/paper/2301.11118/) (2023)
- [Description Logic EL++ Embeddings with Intersectional Closure](https://mlsanity.com/paper/2202.14018/) (2022)
- [Ontology Embedding: A Survey of Methods, Applications and Resources](https://mlsanity.com/paper/2406.10964/) (2024)
- [Predicting Gene-Disease Associations with Knowledge Graph Embeddings over Multiple Ontologies](https://mlsanity.com/paper/2105.04944/) (2021)
- [TransBox: EL++-closed Ontology Embedding](https://mlsanity.com/paper/2410.14571/) (2024)
- [Language Models as Ontology Encoders](https://mlsanity.com/paper/2507.14334/) (2025)
- [Ontology Completion with Natural Language Inference and Concept Embeddings: An Analysis](https://mlsanity.com/paper/2403.17216/) (2024)
- [Prediction of Adverse Biological Effects of Chemicals Using Knowledge Graph Embeddings](https://mlsanity.com/paper/2112.04605/) (2021)
- [TAR: Neural Logical Reasoning across TBox and ABox](https://mlsanity.com/paper/2205.14591/) (2022)
- [FALCON: Scalable Reasoning over Inconsistent ALC Ontologies](https://mlsanity.com/paper/2208.07628/) (2022)
- [Ontology Pre-training for Poison Prediction](https://mlsanity.com/paper/2301.08577/) (2023)
- [Embedding Ontologies via Incorporating Extensional and Intensional Knowledge](https://mlsanity.com/paper/2402.01677/) (2024)
- [A Self-matching Training Method with Annotation Embedding Models for Ontology Subsumption Prediction](https://mlsanity.com/paper/2402.16278/) (2024)
- [Link Analysis meets Ontologies: Are Embeddings the Answer?](https://mlsanity.com/paper/2111.11710/) (2021)
- [Differentiable Fuzzy ALC: A Neural-Symbolic Representation Language for Symbol Grounding](https://mlsanity.com/paper/2211.12006/) (2022)
- [Biomedical Knowledge Graph Embeddings with Negative Statements](https://mlsanity.com/paper/2308.03447/) (2023)
- [Neuro-Symbolic RDF and Description Logic Reasoners: The State-Of-The-Art and Challenges](https://mlsanity.com/paper/2308.04814/) (2023)
- [T-TExTS (Teaching Text Expansion for Teacher Scaffolding): Enhancing Text Selection in High School Literature through Knowledge Graph-Based Recommendation](https://mlsanity.com/paper/2506.12075/) (2025)
- [Lattice-preserving ALC ontology embeddings with saturation](https://mlsanity.com/paper/2305.07163/) (2023)
- [Hierarchical Retrieval with Out-Of-Vocabulary Queries: A Case Study on SNOMED CT](https://mlsanity.com/paper/2511.16698/) (2025)
- [Extending Ontologies: From Dense Embeddings to Hybrid Quantum-Fuzzy Systems](https://mlsanity.com/paper/2606.08658/) (2026)
- [NeurOWL: An LLM-Based Neural-symbolic Framework for Incomplete OWL Ontology Reasoning](https://mlsanity.com/paper/2607.15776/) (2026)

## Abstract (arXiv)

Motivation: Ontologies are widely used in biology for data annotation, integration, and analysis. In addition to formally structured axioms, ontologies contain meta-data in the form of annotation axioms which provide valuable pieces of information that characterize ontology classes. Annotations commonly used in ontologies include class labels, descriptions, or synonyms. Despite being a rich source of semantic information, the ontology meta-data are generally unexploited by ontology-based analysis methods such as semantic similarity measures. Results: We propose a novel method, OPA2Vec, to generate vector representations of biological entities in ontologies by combining formal ontology axioms and annotation axioms from the ontology meta-data. We apply a Word2Vec model that has been pre-trained on PubMed abstracts to produce feature vectors from our collected data. We validate our method in two different ways: first, we use the obtained vector representations of proteins as a similarity measure to predict protein-protein interaction (PPI) on two different datasets. Second, we evaluate our method on predicting gene-disease associations based on phenotype similarity by generating vector representations of genes and diseases using a phenotype ontology, and applying the obtained vectors to predict gene-disease associations. These two experiments are just an illustration of the possible applications of our method. OPA2Vec can be used to produce vector representations of any biomedical entity given any type of biomedical ontology. Availability: https://github.com/bio-ontology-research-group/opa2vec Contact: robert.hoehndorf@kaust.edu.sa and xin.gao@kaust.edu.sa.