@inproceedings{banerjee-etal-2024-cross,
    title = "Cross-Lingual Ontology Matching using Structural and Semantic Similarity",
    author = "Banerjee, Shubhanker  and
      Chakravarthi, Bharathi Raja  and
      McCrae, John Philip",
    editor = "Chiarcos, Christian  and
      Gkirtzou, Katerina  and
      Ionov, Maxim  and
      Khan, Fahad  and
      McCrae, John P.  and
      Ponsoda, Elena Montiel  and
      Chozas, Patricia Mart{\'\i}n",
    booktitle = "Proceedings of the 9th Workshop on Linked Data in Linguistics @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.ldl-1.2",
    pages = "11--21",
    abstract = "The development of ontologies in various languages is attracting attention as the amount of multilingual data available on the web increases. Cross-lingual ontology matching facilitates interoperability amongst ontologies in different languages. Although supervised machine learning-based methods have shown good performance on ontology matching, their application to the cross-lingual setting is limited by the availability of training data. Current state-of-the-art unsupervised methods for cross-lingual ontology matching focus on lexical similarity between entities. These approaches follow a two-stage pipeline where the entities are translated into a common language using a translation service in the first step followed by computation of lexical similarity between the translations to match the entities in the second step. In this paper we introduce a novel ontology matching method based on the fusion of structural similarity and cross-lingual semantic similarity. We carry out experiments using 3 language pairs and report substantial improvements on the performance of the lexical methods thus showing the effectiveness of our proposed approach. To the best of our knowledge this is the first work which tackles the problem of unsupervised ontology matching in the cross-lingual setting by leveraging both structural and semantic embeddings.",
}