Matching with Transformers in MELT

Understand

One of the strongest signals for automated matching of ontologies and knowledge graphs are the textual descriptions of the concepts.

• The methods that are typically applied (such as character- or token-based comparisons) are relatively simple, and therefore do not capture the actual meaning of the texts.
• With the rise of transformer-based language models, text comparison based on meaning (rather than lexical features) is possible.
• In this paper, we model the ontology matching task as classification problem and present approaches based on transformer models.