# OntoEA: Ontology-guided Entity Alignment via Joint Knowledge Graph Embedding

arXiv: https://arxiv.org/abs/2105.07688 · HTML: https://mlsanity.com/paper/2105.07688/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 31 references that are in the map.
- First posted on arXiv 17 May 2021.

## Builds on (most cited first, 17 of 17)

- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Word Translation Without Parallel Data](https://mlsanity.com/paper/1710.04087/) (2017)
- [Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment](https://mlsanity.com/paper/1611.03954/) (2016)
- [Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding](https://mlsanity.com/paper/1708.05045/) (2017)
- [A Benchmarking Study of Embedding-based Entity Alignment for Knowledge Graphs](https://mlsanity.com/paper/2003.07743/) (2020)
- [Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs](https://mlsanity.com/paper/1908.08210/) (2019)
- [Knowledge Graph Alignment Network with Gated Multi-hop Neighborhood Aggregation](https://mlsanity.com/paper/1911.08936/) (2019)
- [Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs](https://mlsanity.com/paper/1905.04914/) (2019)
- [Multi-view Knowledge Graph Embedding for Entity Alignment](https://mlsanity.com/paper/1906.02390/) (2019)
- [OWL2Vec*: Embedding of OWL Ontologies](https://mlsanity.com/paper/2009.14654/) (2020)
- [Universal Representation Learning of Knowledge Bases by Jointly Embedding Instances and Ontological Concepts](https://mlsanity.com/paper/2103.08115/) (2021)
- [Differentiating Concepts and Instances for Knowledge Graph Embedding](https://mlsanity.com/paper/1811.04588/) (2018)
- [Unsupervised Knowledge Graph Alignment by Probabilistic Reasoning and Semantic Embedding](https://mlsanity.com/paper/2105.05596/) (2021)
- [An Industry Evaluation of Embedding-based Entity Alignment](https://mlsanity.com/paper/2010.11522/) (2020)
- [Recurrent Skipping Networks for Entity Alignment](https://mlsanity.com/paper/1811.02318/) (2018)

## Built on by (most cited first, 19 of 19)

- [Multi-modal Contrastive Representation Learning for Entity Alignment](https://mlsanity.com/paper/2209.00891/) (2022)
- [MEAformer: Multi-modal Entity Alignment Transformer for Meta Modality Hybrid](https://mlsanity.com/paper/2212.14454/) (2022)
- [Ontology-enhanced Prompt-tuning for Few-shot Learning](https://mlsanity.com/paper/2201.11332/) (2022)
- [Chain-of-Layer: Iteratively Prompting Large Language Models for Taxonomy Induction from Limited Examples](https://mlsanity.com/paper/2402.07386/) (2024)
- [Dual Box Embeddings for the Description Logic EL++](https://mlsanity.com/paper/2301.11118/) (2023)
- [Ontology Embedding: A Survey of Methods, Applications and Resources](https://mlsanity.com/paper/2406.10964/) (2024)
- [Entity Alignment with Reliable Path Reasoning and Relation-Aware Heterogeneous Graph Transformer](https://mlsanity.com/paper/2205.08806/) (2022)
- [Learning from Negative Samples in Biomedical Generative Entity Linking](https://mlsanity.com/paper/2408.16493/) (2024)
- [generAItor: Tree-in-the-Loop Text Generation for Language Model Explainability and Adaptation](https://mlsanity.com/paper/2403.07627/) (2024)
- [Joint Multilingual Knowledge Graph Completion and Alignment](https://mlsanity.com/paper/2210.08922/) (2022)
- [AsyncET: Asynchronous Learning for Knowledge Graph Entity Typing with Auxiliary Relations](https://mlsanity.com/paper/2308.16055/) (2023)
- [Improving Biomedical Entity Linking with Retrieval-enhanced Learning](https://mlsanity.com/paper/2312.09806/) (2023)
- [Biomedical Entity Linking as Multiple Choice Question Answering](https://mlsanity.com/paper/2402.15189/) (2024)
- [PRASEMap: A Probabilistic Reasoning and Semantic Embedding based Knowledge Graph Alignment System](https://mlsanity.com/paper/2106.08801/) (2021)
- [Type-enhanced Ensemble Triple Representation via Triple-aware Attention for Cross-lingual Entity Alignment](https://mlsanity.com/paper/2305.01556/) (2023)
- [OTIEA:Ontology-enhanced Triple Intrinsic-Correlation for Cross-lingual Entity Alignment](https://mlsanity.com/paper/2305.01561/) (2023)
- [Integration of Contextual Descriptors in Ontology Alignment for Enrichment of Semantic Correspondence](https://mlsanity.com/paper/2411.19113/) (2024)
- [FLORA: Unsupervised Knowledge Graph Alignment by Fuzzy Logic](https://mlsanity.com/paper/2510.20467/) (2025)
- [Are a Thousand Words Better Than a Single Picture? Beyond Images -- A Framework for Multi-Modal Knowledge Graph Dataset Enrichment](https://mlsanity.com/paper/2603.16974/) (2026)

## Abstract (arXiv)

Semantic embedding has been widely investigated for aligning knowledge graph (KG) entities. Current methods have explored and utilized the graph structure, the entity names and attributes, but ignore the ontology (or ontological schema) which contains critical meta information such as classes and their membership relationships with entities. In this paper, we propose an ontology-guided entity alignment method named OntoEA, where both KGs and their ontologies are jointly embedded, and the class hierarchy and the class disjointness are utilized to avoid false mappings. Extensive experiments on seven public and industrial benchmarks have demonstrated the state-of-the-art performance of OntoEA and the effectiveness of the ontologies.