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. We further provide an easy to use implementation in the MELT framework which is suited for ontology and knowledge graph matching. We show that a transformer-based filter helps to choose the correct correspondences given a high-recall alignment and already achieves a good result with simple alignment post-processing methods.
- Pub Date:
- September 2021
- Computer Science - Computation and Language;
- Computer Science - Artificial Intelligence;
- Computer Science - Information Retrieval;
- Computer Science - Machine Learning
- accepted at the Ontology Matching Workshop at the International Semantic Web Conference (ISWC 2021)