  Ontology Alignment is an important research problem that finds application in
various fields such as data integration, data transfer, data preparation etc.
State-of-the-art (SOTA) architectures in Ontology Alignment typically use naive
domain-dependent approaches with handcrafted rules and manually assigned
values, making them unscalable and inefficient. Deep Learning approaches for
ontology alignment use domain-specific architectures that are not only
in-extensible to other datasets and domains, but also typically perform worse
than rule-based approaches due to various limitations including over-fitting of
models, sparsity of datasets etc. In this work, we propose VeeAlign, a Deep
Learning based model that uses a dual-attention mechanism to compute the
contextualized representation of a concept in order to learn alignments. By
doing so, not only does our approach exploit both syntactic and semantic
structure of ontologies, it is also, by design, flexible and scalable to
different domains with minimal effort. We validate our approach on various
datasets from different domains and in multilingual settings, and show its
superior performance over SOTA methods.
