Few-Shot Ontology Alignment Model with Attribute Attentions

Nowadays, explosive growth of ontologies are used for managing data in various domains. They usually own different vocabularies and structures following different fashions. Ontology alignment finding semantic correspondences between elements of these ontologies can effectively facilitate the data communication and novel application creation in many practical scenarios. However, we noticed that, the traditional parametric ontology mapping methods still depend on individualistic abilities for setting proper parameters for mapping. When trying to utilize artificial neural networks for the automatic ontology mapping, the training data are found insufficient in most of the cases. This paper analyzes these problems, and proposes a few-shot ontology alignment model, which can automatically learn how to map two ontologies from only a few training links between their element pairs. The proposed model applies the Siamese neural network in computer vision on ontology alignment and designs an attention detection network learning the attention weights for different ontology attributes. A few experiments that conducted on the anatomy ontology alignment show that our model achieves good performance (94.3% of F-measure) with 200 training alignments without traditional parametric setting.