# DAEOM: A Deep Attentional Embedding Approach for Biomedical Ontology Matching
DOME [22] is a scalable matcher that relies on large texts describing ontological concepts. It uses the doc2 vec approach to train a fixed-length vector representation of the concepts. Mappings are generated if two concepts are close to each other in the resulting vector space. [PERSON] et al. [23] proposed an entity representation learning algorithm based on Stacked Auto-Encoders to learn the general representation of the entities. To describe an ontological entity, they design a combination of its ID, labels, and comments. They introduced an iterative similarity propagation method that takes advantage of the more abundant structure information of ontology to discover more mappings. [PERSON] et al. [24] proposed a neural architecture tailored for biomedical ontology matching called OntoEmma, which can encode a variety of information and derive large amounts of labeled data for training the model. Moreover, they utilize natural language texts associated with entities to further improve the quality of alignments. [PERSON] et al. [17] present an alternative ontology matching framework called MultiOM, in which different loss functions were designed based on cross-entropy to model different views among ontologies and learn the vector representations of concepts. They further proposed a novel negative sampling skill tailored for structural relations, which could obtain better vector representations of concepts. After computing the semantic similarity of entities from two compared ontologies with the learned vectors, the final alignment method is executed to select appropriate correspondences between entities of compared ontologies. The final alignment is an important part of the ontology matching process because it directly determines the output result of this process. Several works [17; 19; 20; 23] use the Stable Marriage algorithm [25] for mapping selection during the final alignment. They iteratively pass through all the candidate alignments and discard those with a cosine distance higher than a certain threshold. The adjustment of the threshold value is usually determined manually within matching systems considering the experience of developers. However, there exist two limitations for the above methods. The first one is the sparsity problem of structural relations. To avoid the poor capability of encoding sparse relations, the above methods prefer terminological-based features to learn concept vectors for discovering mappings, but they do not make full use of structural relations in ontologies. Although [PERSON] et al. [17] designed a negative sampling technique to fine-tune the vector representation of concepts by using the network structure indirectly, it can not effectively preserve the network structure. The other is that using the Stable Marriage algorithm during the final alignment has worst-case time complexity \(\Theta\left(n^{2}\log n\right)\)[20]. That is, the larger the ontology, the more time it takes for the final alignment. Meanwhile, the adjustment of the threshold value is usually determined manually within matching systems considering the experience of developers, therefore it is not necessarily adjusted in an optimal way for each pair of compared ontologies [26]. Motivated by the above observations, in this paper we propose a graph attentional autoencoder-based attributed ontology matching framework, which models the matching process by jointly encoding terminological description and network structure to continuous vector representation. Based on the representation, a mapping selection module is proposed to perform the matching algorithm towards better performance. We summarize our contributions as follows:  
* We develop the first siamese graph attention-based autoencoder to effectively integrate both network structure and terminological description for deep latent representation learning in ontology matching.