Biomedical ontology alignment: An approach based on representation learning

While 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. An 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

A Linguistic Approach to Aligning Representations of Human Anatomy and Radiology

To realize applications such as semantic medical image search different domain ontologies are necessary that provide complementary knowledge about human anatomy and radiology. Consequently, integration of these different but nevertheless related types of medical knowledge from disparate domain ontologies becomes necessary. Ontology alignment is one way to achieve this objective. Our approach for aligning medical ontologies has three aspects: (a) linguistic-based, (b) corpus-based, and (c) dialogue-based. We briefly report on the linguistic alignment (i.e. the first aspect) using an ontology on human anatomy and a terminology on radiolog

A Linguistic Approach to Aligning Representations of Human Anatomy and Radiology

To realize applications such as semantic medical image search different domain ontologies are necessary that provide complementary knowledge about human anatomy and radiology. Consequently, integration of these different but nevertheless related types of medical knowledge from disparate domain ontologies becomes necessary. Ontology alignment is one way to achieve this objective. Our approach for aligning medical ontologies has three aspects: (a) linguistic-based, (b) corpus-based, and (c) dialogue-based. We briefly report on the linguistic alignment (i.e. the first aspect) using an ontology on human anatomy and a terminology on radiology

A semi-automatic semantic method for mapping SNOMED CT concepts to VCM Icons

VCM (Visualization of Concept in Medicine) is an iconic language for representing key medical concepts by icons. However, the use of this language with reference terminologies, such as SNOMED CT, will require the mapping of its icons to the terms of these terminologies. Here, we present and evaluate a semi-automatic semantic method for the mapping of SNOMED CT concepts to VCM icons. Both SNOMED CT and VCM are compositional in nature; SNOMED CT is expressed in description logic and VCM semantics are formalized in an OWL ontology. The proposed method involves the manual mapping of a limited number of underlying concepts from the VCM ontology, followed by automatic generation of the rest of the mapping. We applied this method to the clinical findings of the SNOMED CT CORE subset, and 100 randomly-selected mappings were evaluated by three experts. The results obtained were promising, with 82 of the SNOMED CT concepts correctly linked to VCM icons according to the experts. Most of the errors were easy to fix

Framework for Enhanced Ontology Alignment using BERT-Based

This framework combines a few approaches to improve ontology alignment by using the data mining method with BERT. The method utilizes data mining techniques to identify the optimal characteristics for picking the data attributes of instances to match ontologies. Furthermore, this framework was developed to improve current precision and recall measures for ontology matching techniques. Since knowledge integration began, the main requirement for ontology alignment has always been syntactic and structural matching. This article presents a new approach that employs advanced methods like data mining and BERT embeddings to produce more expansive and contextually aware ontology alignment. The proposed system exploits contextual representation of BERT, semantic understanding, feature extraction, and pattern recognition through data mining techniques. The objective is to combine data-driven insights with semantic representation advantages to enhance accuracy and efficiency in the ontology alignment process. The evaluation conducted using annotated datasets as well as traditional approaches demonstrates how effective and adaptable, according to domains, our proposed framework is across several domains

Integrating Genomic Knowledge Sources through an Anatomy Ontology

Modern genomic research has access to a plethora of knowledge sources. Often, it is imperative that researchers combine and integrate knowledge from multiple perspectives. Although some technology exists for connecting data and knowledge bases, these methods are only just begin-ning to be successfully applied to research in modern cell biology. In this paper, we argue that one way to integrate multiple knowledge sources is through anatomy—both generic cellular anatomy, as well as anatomic knowledge about the tissues and organs that may be studied via microarray gene expression experiments. We present two examples where we have combined a large ontology of human anatomy (the FMA) with other genomic knowledge sources: the gene ontology (GO) and the mouse genomic databases (MGD) of the Jackson Labs. These two initial examples of knowledge integration provide a proof of concept that anatomy can act as a hub through which we can usefully combine a variety of genomic knowledge and data

Genetics and language: a neurobiological perspective on the missing link (-ing hypotheses)

The paper argues that both evolutionary and genetic approaches to studying the biological foundations of speech and language could benefit from fractionating the problem at a finer grain, aiming not to map genetics to “language”—or even subdomains of language such as “phonology” or “syntax”—but rather to link genetic results to component formal operations that underlie processing the comprehension and production of linguistic representations. Neuroanatomic and neurophysiological research suggests that language processing is broken down in space (distributed functional anatomy along concurrent pathways) and time (concurrent processing on multiple time scales). These parallel neuronal pathways and their local circuits form the infrastructure of speech and language and are the actual targets of evolution/genetics. Therefore, investigating the mapping from gene to brain circuit to linguistic phenotype at the level of generic computational operations (subroutines actually executable in these circuits) stands to provide a new perspective on the biological foundations in the healthy and challenged brain