Semantic Alignment of Multilingual Knowledge Graphs via Contextualized Vector Projections

The paper presents a cross-lingual ontology alignment system that uses embedding-based cosine similarity matching. Ontology entities are contextually enriched through novel techniques, employing a fine-tuned transformer-based multilingual model for generating better embeddings. Evaluated on the OAEI-2022 multifarm track, it achieves a 71% F1 score, demonstrating its ability to capture subtle cross-lingual similarities.