# Ontology Matching with Knowledge Rules

arXiv: https://arxiv.org/abs/1507.03097 · HTML: https://mlsanity.com/paper/1507.03097/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2015.

## Built on by (most cited first, 4 of 4)

- [VeeAlign: Multifaceted Context Representation using Dual Attention for Ontology Alignment](https://mlsanity.com/paper/2102.04081/) (2021)
- [Uncertainty Management in the Construction of Knowledge Graphs: a Survey](https://mlsanity.com/paper/2405.16929/) (2024)
- [CMOMgen: Complex Multi-Ontology Alignment via Pattern-Guided In-Context Learning](https://mlsanity.com/paper/2510.21656/) (2025)
- [MAGE-KT: Multi-Agent Graph-Enhanced Knowledge Tracing with Subgraph Retrieval and Asymmetric Fusion](https://mlsanity.com/paper/2601.16886/) (2026)

## Abstract (arXiv)

Ontology matching is the process of automatically determining the semantic equivalences between the concepts of two ontologies. Most ontology matching algorithms are based on two types of strategies: terminology-based strategies, which align concepts based on their names or descriptions, and structure-based strategies, which exploit concept hierarchies to find the alignment. In many domains, there is additional information about the relationships of concepts represented in various ways, such as Bayesian networks, decision trees, and association rules. We propose to use the similarities between these relationships to find more accurate alignments. We accomplish this by defining soft constraints that prefer alignments where corresponding concepts have the same local relationships encoded as knowledge rules. We use a probabilistic framework to integrate this new knowledge-based strategy with standard terminology-based and structure-based strategies. Furthermore, our method is particularly effective in identifying correspondences between complex concepts. Our method achieves substantially better F-score than the previous state-of-the-art on three ontology matching domains.