# Background Knowledge in Schema Matching: Strategy vs. Data

arXiv: https://arxiv.org/abs/2107.00001 · HTML: https://mlsanity.com/paper/2107.00001/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 40 references that are in the map.
- First posted on arXiv 29 Jun 2021.

## Builds on (most cited first, 5 of 5)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [RDF2Vec Light -- A Lightweight Approach for Knowledge Graph Embeddings](https://mlsanity.com/paper/2009.07659/) (2020)
- [KGvec2go -- Knowledge Graph Embeddings as a Service](https://mlsanity.com/paper/2003.05809/) (2020)
- [Supervised Ontology and Instance Matching with MELT](https://mlsanity.com/paper/2009.11102/) (2020)

## Built on by (most cited first, 3 of 3)

- [KAE: A Property-based Method for Knowledge Graph Alignment and Extension](https://mlsanity.com/paper/2407.05320/) (2024)
- [Recognizing Entity Types via Properties](https://mlsanity.com/paper/2304.07910/) (2023)
- [Matching of Descriptive Labels to Glossary Descriptions](https://mlsanity.com/paper/2310.18385/) (2023)

## Abstract (arXiv)

The use of external background knowledge can be beneficial for the task of matching schemas or ontologies automatically. In this paper, we exploit six general-purpose knowledge graphs as sources of background knowledge for the matching task. The background sources are evaluated by applying three different exploitation strategies. We find that explicit strategies still outperform latent ones and that the choice of the strategy has a greater impact on the final alignment than the actual background dataset on which the strategy is applied. While we could not identify a universally superior resource, BabelNet achieved consistently good results. Our best matcher configuration with BabelNet performs very competitively when compared to other matching systems even though no dataset-specific optimizations were made.