Biomedical ontology alignment: an approach based on representation learning

Abstract

BackgroundWhile representation learning techniques have shown great promise in application to a number of different NLP tasks, they have had little impact on the problem of ontology matching. Unlike past work that has focused on feature engineering, we present a novel representation learning approach that is tailored to the ontology matching task. Our approach is based on embedding ontological terms in a high-dimensional Euclidean space. This embedding is derived on the basis of a novel phrase retrofitting strategy through which semantic similarity information becomes inscribed onto fields of pre-trained word vectors. The resulting framework also incorporates a novel outlier detection mechanism based on a denoising autoencoder that is shown to improve performance.ResultsAn ontology matching system derived using the proposed framework achieved an F-score of 94% on an alignment scenario involving the Adult Mouse Anatomical Dictionary and the Foundational Model of Anatomy ontology (FMA) as targets. This compares favorably with the best performing systems on the Ontology Alignment Evaluation Initiative anatomy challenge. We performed additional experiments on aligning FMA to NCI Thesaurus and to SNOMED CT based on a reference alignment extracted from the UMLS Metathesaurus. Our system obtained overall F-scores of 93.2% and 89.2% for these experiments, thus achieving state-of-the-art results.ConclusionsOur proposed representation learning approach leverages terminological embeddings to capture semantic similarity. Our results provide evidence that the approach produces embeddings that are especially well tailored to the ontology matching task, demonstrating a novel pathway for the problem.

Semantic Mediation Model to Promote Improved Data Sharing Using Representation Learning in Heterogeneous Healthcare Service Environments

“…The ontology alignment (SA) process is to discover the semantic association among two diverse ontologies, where the first one is the initial source, and the other one is target ontology [49,52]. We We describe the formal expression of each entity information that is used in our model (Equations (1)-(6)).…”

“…The ontology alignment (SA) process is to discover the semantic association among two diverse ontologies, where the first one is the initial source, and the other one is target ontology [49,52]. We Representation learning provides the opportunity to support semantic interoperabili through learning representation of ontological concepts, and this has been envisaged improved performance of deep learning methods.…”

“…However, recently, the ontology alignment process is moving towards learning-based approaches. Research, such as in [49,52], have realized that the deep neural network-based approaches prove better in ontology alignment using representation learning. We utilize a similar approach to get the learned representation of ontological entities using a model of Auto Encoder (AuE) [56], which learns the abstract representation on the VO data.…”

“…Going through transformation process, the proposed model acquires the representations for each entity in the VO ontology. Moreover, for learning the description of entities and their correlation, the model minimizes loss function for VO ontology set of class entities , , Ô, given the weights (w) and bias (b), where the custom build function minimizes the loss between VO class descriptions of and Ô as per standard procedure[50,52]. A similar operation is performed for the data properties ( representation of the (B-B-Ws) of entities in the VO source and target ontologies.…”

Approximation algorithms in combinatorial scientific computing

“…In this case, exact matchings are not required, and its use increases the run time of the algorithm, limiting the size of the problems that could be solved. This is the case for network alignment (Khan, Gleich, Pothen and Halappanavar 2012), for adaptive anonymity (Khan et al 2018a), k-nearest neighbour graph construction (Ferdous, Pothen and Khan 2018), ontology alignment (Kolyvakis, Kalousis, Smith and Kiritsis 2018), etc.…”