  Ontology Alignment is an important research problem applied to various fields
such as data integration, data transfer, data preparation, etc.
State-of-the-art (SOTA) Ontology Alignment systems typically use naive
domain-dependent approaches with handcrafted rules or domain-specific
architectures, making them unscalable and inefficient. In this work, we propose
VeeAlign, a Deep Learning based model that uses a novel dual-attention
mechanism to compute the contextualized representation of a concept which, in
turn, is used to discover alignments. By doing this, not only is our approach
able to exploit both syntactic and semantic information encoded in ontologies,
it is also, by design, flexible and scalable to different domains with minimal
effort. We evaluate our model on four different datasets from different domains
and languages, and establish its superiority through these results as well as
detailed ablation studies. The code and datasets used are available at
https://github.com/Remorax/VeeAlign.
