# DAEOM: A Deep Attentional Embedding Approach for Biomedical Ontology Matching  
[PERSON]  
1 State Key Laboratory of Software Development Environment, Beihang University, Beijing 100091, China; [EMAIL_ADDRESS] (J.W. ); slma@nlsde. generate correspondences which express more complex relationships between entities from different ontologies [9]. In this paper, we focus on matching single entities. Early studies regarding automatic ontology matching has focused on engineering features from terminological, structural, extensional (individuals of concepts) information, and external resource [10; 11]. These features are utilized to compute the similarities of ontological entities (i.e., concepts, properties, individuals) for guiding ontology matching. Up to now, the mainstream ontology matching systems (e.g., AML [12], FCA_Map [13], LogMap [14], and XMap [15]) still use feature-based strategies to evaluate entity similarity. AML employs various sophisticated features and domain-specific thesauruses to perform ontology matching based on heuristic methods that rely on aggregation functions. FCA_Map uses Formal Concept Analysis to derive terminological hierarchical structures that are represented as lattices. The matching is performed by aligning the constructed lattices, taking into account the lexical and structural information that they incorporate. LogMap uses logic-based reasoning over the extracted features and cast ontology matching to a satisfiability problem. XMap relies on the context notion to deal with lexical ambiguity as well as a parallel comparison between concepts to efficiently handle the matching of large ontologies. Feature-based methods mainly employ crafting features of the data to achieve specific tasks. Unfortunately determining which hand-crafted features will be valuable for a given task can be highly time consuming. [PERSON] and [PERSON] showed that the performance of ontology matching based on such engineered features varies greatly with the domain described by ontologies [16]. As a complement to feature engineering, attempts have been made to develop representation learning techniques for ontology matching. Continuous vectors representing ontological entities can capture the potential associations among features, which is helpful to discover more mappings among ontologies [17]. Representation learning have so far limited impacts on ontology matching, specifically in biomedical ontologies. To the best of our knowledge, only seven literatures have explored the use of representation learning techniques for ontology matching. In [18; 19; 20], they used word embeddings [21] to compute the semantic similarities among elements. [PERSON] et al. [18] firstly introduce word embeddings to the field of ontology matching. They trained word embeddings on Wikipedia and used skip-gram architectures for computing vector representations. They combined edit distance and word embeddings for the computation of the semantic similarities among elements. For textual entity descriptions consisting of more than one word, they proposed a heuristic method to compute semantic similarities among elements. The entity matching strategy was based on maximum similarity. For every entity in the source ontology, the algorithm found the most similar entity in the target ontology. To avoid the problems that come from the semantic similarity and conceptual association coalescence, in [19; 20], they used synonymy and antonymy constraints extracted from semantic lexicons to refine pre-trained word embeddings and make them better suited for evaluating semantic similarity. [PERSON] et al. [19] used the Dual Embedding Space Model (DESM) to compute the semantic similarity of two entity textual description word sets. In [20], they averaged the bag of the word vectors to produce sentence vectors of the entities' textual description, and use the cosine distance to compute the semantic similarity of two entities. In addition, they leverage an outlier detection mechanism based on a denoising autoencoder to improve the performance of alignments.