🤖 AI Summary
This work addresses the challenge of cross-lingual semantic alignment of ontology entities in multilingual knowledge graphs by proposing a context-enhanced alignment approach. It constructs semantically rich multilingual entity descriptions and fine-tunes a multilingual Transformer model to generate high-quality embeddings. Precise alignments are then achieved through cosine similarity matching combined with an adaptive threshold filtering mechanism. Evaluated on the OAEI-2022 Multifarm track, the method achieves an F1 score of 71% (recall: 78%, precision: 65%), outperforming the best baseline by 16% and significantly improving both accuracy and robustness in cross-lingual ontology alignment.
📝 Abstract
The paper presents our work on cross-lingual ontology alignment system which uses embedding based cosine similarity matching. The ontology entities are made contextually richer by creating descriptions using novel techniques. We use a fine-tuned transformer based multilingual model for generating better embeddings. We use cosine similarity to find positive ontology entities pairs and then apply threshold filtering to retain only highly similar entities. We have evaluated our work on OAEI-2022 multifarm track. We achieve 71% F1 score (78% recall and 65% precision) on the evaluation dataset, 16% increase from best baseline score. This suggests that our proposed alignment pipeline is able to capture the subtle cross-lingual similarities.