Biomedical Ontologies Research Articles

A substring replacement approach for identifying missing IS-A relations in SNOMED CT.

Biomedical ontologies provide formalized information and knowledge in the biomedical domain. Over the years, biomedical ontologies have played an important role in facilitating biomedical research and applications. Common quality issues of biomedical ontologies include inconsistent naming of concepts, redundant concepts, redundant relations, incomplete/incorrect concept definitions, and incomplete/incorrect class hierarchies. In this work, we focus on addressing the incompleteness of the class hierarchy in SNOMED CT. We develop a substring replacement approach, leveraging concepts' lexical features and existing IS-A relations to identify potential missing IS-A relations in SNOMED CT. To evaluate the effectiveness of our approach, we performed both automated and manual validation. For the automated evaluation, we leverage relations from external terminologies in the Unified Medical Language System (UMLS) to validate the identified missing IS-A relations. For the manual validation, a randomly selected 100 samples from the results are reviewed by a domain expert. Applying our approach to the March 2022 release of SNOMED CT US Edition, we identified 3,228 potential missing IS-A relations, among which 63 were validated through the UMLS. The evaluation by the domain expert revealed that 89 out of 100 (a precision of 89%) missing IS-A relations are valid cases, showing the effectiveness of this substring replacement approach to facilitate the quality assurance of IS-A relations in SNOMED CT.

Improving graph embeddings via entity linking: A case study on Italian clinical notes

The ever-increasing availability of Electronic Health Records (EHRs) is the key enabling factor of precision medicine, which aims to provide therapies and diagnoses based not only on medical literature, but also on clinical experience and individual information of patients (e.g. genomics, lifestyle, health history). The unstructured nature of EHRs has posed several challenges on their effective analysis, and heterogeneous graphs are the most suitable solution to handle the heterogeneity of information contained in EHRs. However, while EHRs are an extremely valuable data source, information from current medical literature has yet to be considered in clinical decision support systems. In this work, we build an heterogeneous graph from Italian EHRs provided by the Hospital of Naples Federico II, and we define a methodological workflow allowing us to predict the presence of a link between patients and diagnosed diseases. We empirically demonstrate that linking concepts to biomedical ontologies (e.g. UMLS, DBpedia) — which allow us to extract entities and relationships from medical literature — is significantly beneficial to our link-prediction workflow in terms of Area Under the ROC curve (AUC) and Mean Reciprocal Rank (MRR).

Ontology-based feature engineering in machine learning workflows for heterogeneous epilepsy patient records

Biomedical ontologies are widely used to harmonize heterogeneous data and integrate large volumes of clinical data from multiple sources. This study analyzed the utility of ontologies beyond their traditional roles, that is, in addressing a challenging and currently underserved field of feature engineering in machine learning workflows. Machine learning workflows are being increasingly used to analyze medical records with heterogeneous phenotypic, genotypic, and related medical terms to improve patient care. We performed a retrospective study using neuropathology reports from the German Neuropathology Reference Center for Epilepsy Surgery at Erlangen, Germany. This cohort included 312 patients who underwent epilepsy surgery and were labeled with one or more diagnoses, including dual pathology, hippocampal sclerosis, malformation of cortical dysplasia, tumor, encephalitis, and gliosis. We modeled the diagnosis terms together with their microscopy, immunohistochemistry, anatomy, etiologies, and imaging findings using the description logic-based Web Ontology Language (OWL) in the Epilepsy and Seizure Ontology (EpSO). Three tree-based machine learning models were used to classify the neuropathology reports into one or more diagnosis classes with and without ontology-based feature engineering. We used five-fold cross validation to avoid overfitting with a fixed number of repetitions while leaving out one subset of data for testing, and we used recall, balanced accuracy, and hamming loss as performance metrics for the multi-label classification task. The epilepsy ontology-based feature engineering approach improved the performance of all the three learning models with an improvement of 35.7%, 54.5%, and 33.3% in logistics regression, random forest, and gradient tree boosting models respectively. The run time performance of all three models improved significantly with ontology-based feature engineering with gradient tree boosting model showing a 93.8% reduction in the time required for training and testing of the model. Although, all three models showed an overall improved performance across the three-performance metrics using ontology-based feature engineering, the rate of improvement was not consistent across all input features. To analyze this variation in performance, we computed feature importance scores and found that microscopy had the highest importance score across the three models, followed by imaging, immunohistochemistry, and anatomy in a decreasing order of importance scores. This study showed that ontologies have an important role in feature engineering to make heterogeneous clinical data accessible to machine learning models and also improve the performance of machine learning models in multilabel multiclass classification tasks.

Food process ontology requirements

People often value the sensual, celebratory, and health aspects of food, but behind this experience exists many other value-laden agricultural production, distribution, manufacturing, and physiological processes that support or undermine a healthy population and a sustainable future. The complexity of such processes is evident in both every-day food preparation of recipes and in industrial food manufacturing, packaging and storage, each of which depends critically on human or machine agents, chemical or organismal ingredient references, and the explicit instructions and implicit procedures held in formulations or recipes. An integrated ontology landscape does not yet exist to cover all the entities at work in this farm to fork journey. It seems necessary to construct such a vision by reusing expert-curated fit-to-purpose ontology subdomains and their relationship, material, and more abstract organization and role entities. The challenge is to make this merger be, by analogy, one language, rather than nouns and verbs from a dozen or more dialects which cannot be used directly in statements about some aspect of the farm to fork journey without expensive translation or substantial dialect education in order to understand a particular text or domain of knowledge. This work focuses on the ontology components – object and data properties and annotations – needed to model food processes or more general process modelling within the context of the Open Biological and Biomedical Ontology Foundry and congruent ontologies. Ideally these components can be brought together in a general process ontology that can be specialized not only for the food domain but for carrying out other protocols as well. Many operations involved in food identification, preparation, transportation and storage – shaking, boiling, mixing, freezing, labeling, shipping – are actually common to activities from manufacturing and laboratory work to local or home food preparation.

129. Standardization of cancer terminology in the Mondo Disease Ontology

Biomedical ontologies provide a standard computable representation of knowledge that can be used to integrate and navigate large amounts of heterogeneous data for downstream computational analysis and knowledge discovery. The Mondo Disease Ontology (Mondo) is a semantic resource that integrates several existing disease terminologies, provides precise, curated semantic mappings between them, and unifies them into one coherent classification of diseases. Cancer concepts in Mondo are formally classified into a hierarchical representation, which can be used to annotate data at different levels of granularity. Neoplasm is the top-level class and subtypes (subclasses) are largely aligned with the NCIt neoplasm branch, with malignant neoplasms (cancers) being classified as subtypes. Classes are grouped in multiple ways, such as by anatomical entities affected (such as cardiovascular or digestive system neoplasms), by malignancy (benign, pre-malignant, or malignant, or onset (childhood neoplasm). In addition, Mondo has representations of susceptibilities to cancers and hereditary neoplastic syndromes such as Lynch Syndrome and Li-Fraumeni syndrome. Mondo provides mappings to other disease resources such as the National Cancer Institute Thesaurus (NCIt), as well as cancer terminology from Orphanet, OMIM, Disease Ontology, and others. Mondo precisely annotates each mapping using strict semantics, so that we know when two diseases are precisely equivalent or merely closely related. Mondo is iteratively developed and revised and we invite the community to contribute to Mondo; visit github.com/monarch-initiative/mondo for details.

Ontology for Symptomatic Treatment of Multiple Sclerosis.

Symptomatic treatment is an essential component in the overall treatment of multiple sclerosis (MS). However, knowledge in this regard is confusing and scattered. Physicians also have challenges in choosing symptomatic treatment based on the patient's condition. To share, update, and reuse this knowledge, the aim of this study was to provide an ontology for MS symptomatic treatment. The Symptomatic Treatment of Multiple Sclerosis Ontology (STMSO) was developed according to Ontology Development 101 and a guideline for developing good ontologies in the biomedical domain. We obtained knowledge and rules through a systematic review and entered this knowledge in the form of classes and subclasses in the ontology. We then mapped the ontology using the Basic Formal Ontology (BFO) and Ontology for General Medical Sciences (OGMS) as reference ontologies. The ontology was built using Protégé Editor in the Web Ontology Language format. Finally, an evaluation was done by experts using criterion-based approaches in terms of accuracy, clarity, consistency, and completeness. The knowledge extraction phase identified 110 articles related to the ontology in the form of 626 classes, 40 object properties, and 139 rules. Five general classes included "patient," "symptoms," "pharmacological treatment," "treatment plan," and "measurement index." The evaluation in terms of standards for biomedical ontology showed that STMSO was accurate, clear, consistent, and complete. STMSO is the first comprehensive semantic representation of the symptomatic treatment of MS and provides a major step toward the development of intelligent clinical decision support systems for symptomatic MS treatment.

A comprehensive update on CIDO: the community-based coronavirus infectious disease ontology

BackgroundThe current COVID-19 pandemic and the previous SARS/MERS outbreaks of 2003 and 2012 have resulted in a series of major global public health crises. We argue that in the interest of developing effective and safe vaccines and drugs and to better understand coronaviruses and associated disease mechenisms it is necessary to integrate the large and exponentially growing body of heterogeneous coronavirus data. Ontologies play an important role in standard-based knowledge and data representation, integration, sharing, and analysis. Accordingly, we initiated the development of the community-based Coronavirus Infectious Disease Ontology (CIDO) in early 2020.ResultsAs an Open Biomedical Ontology (OBO) library ontology, CIDO is open source and interoperable with other existing OBO ontologies. CIDO is aligned with the Basic Formal Ontology and Viral Infectious Disease Ontology. CIDO has imported terms from over 30 OBO ontologies. For example, CIDO imports all SARS-CoV-2 protein terms from the Protein Ontology, COVID-19-related phenotype terms from the Human Phenotype Ontology, and over 100 COVID-19 terms for vaccines (both authorized and in clinical trial) from the Vaccine Ontology. CIDO systematically represents variants of SARS-CoV-2 viruses and over 300 amino acid substitutions therein, along with over 300 diagnostic kits and methods. CIDO also describes hundreds of host-coronavirus protein-protein interactions (PPIs) and the drugs that target proteins in these PPIs. CIDO has been used to model COVID-19 related phenomena in areas such as epidemiology. The scope of CIDO was evaluated by visual analysis supported by a summarization network method. CIDO has been used in various applications such as term standardization, inference, natural language processing (NLP) and clinical data integration. We have applied the amino acid variant knowledge present in CIDO to analyze differences between SARS-CoV-2 Delta and Omicron variants. CIDO's integrative host-coronavirus PPIs and drug-target knowledge has also been used to support drug repurposing for COVID-19 treatment.ConclusionCIDO represents entities and relations in the domain of coronavirus diseases with a special focus on COVID-19. It supports shared knowledge representation, data and metadata standardization and integration, and has been used in a range of applications.

Exploration of biomedical knowledge for recurrent glioblastoma using natural language processing deep learning models

BackgroundEfficient exploration of knowledge for the treatment of recurrent glioblastoma (GBM) is critical for both clinicians and researchers. However, due to the large number of clinical trials and published articles, searching for this knowledge is very labor-intensive. In the current study, using natural language processing (NLP), we analyzed medical research corpora related to recurrent glioblastoma to find potential targets and treatments.MethodsWe fine-tuned the ‘SAPBERT’, which was pretrained on biomedical ontologies, to perform question/answering (QA) and name entity recognition (NER) tasks for medical corpora. The model was fine-tuned with the SQUAD2 dataset and multiple NER datasets designed for QA task and NER task, respectively. Corpora were collected by searching the terms “recurrent glioblastoma” and “drug target”, published from 2000 to 2020 in the Web of science (N = 288 articles). Also, clinical trial corpora were collected from ‘clinicaltrial.gov’ using the searching term of ‘recurrent glioblastoma” (N = 587 studies).ResultsFor the QA task, the model showed an F1 score of 0.79. For the NER task, the model showed F1 scores of 0.90 and 0.76 for drug and gene name recognition, respectively. When asked what the molecular targets were promising for recurrent glioblastoma, the model answered that RTK inhibitors or LPA-1 antagonists were promising. From collected clinical trials, the model summarized them in the order of bevacizumab, temozolomide, lomustine, and nivolumab. Based on published articles, the model found the many drug-gene pairs with the NER task, and we presented them with a circus plot and related summarization (https://github.com/bigwiz83/NLP_rGBM).ConclusionUsing NLP deep learning models, we could explore potential targets and treatments based on medical research and clinical trial corpora. The knowledge found by the models may be used for treating recurrent glioblastoma.

Ontology Development Kit: a toolkit for building, maintaining and standardizing biomedical ontologies.

Similar to managing software packages, managing the ontology life cycle involves multiple complex workflows such as preparing releases, continuous quality control checking and dependency management. To manage these processes, a diverse set of tools is required, from command-line utilities to powerful ontology-engineering environmentsr. Particularly in the biomedical domain, which has developed a set of highly diverse yet inter-dependent ontologies, standardizing release practices and metadata and establishing shared quality standards are crucial to enable interoperability. The Ontology Development Kit (ODK) provides a set of standardized, customizable and automatically executable workflows, and packages all required tooling in a single Docker image. In this paper, we provide an overview of how the ODK works, show how it is used in practice and describe how we envision it driving standardization efforts in our community. Database URL: https://github.com/INCATools/ontology-development-kit

Interdisciplinary IoT and Emotion Knowledge Graph-Based Recommendation System to Boost Mental Health

Humans are feeling emotions every day, but they can still encounter difficulties understanding them. To better understand emotions, we integrated interdisciplinary knowledge about emotions from various domains such as neurosciences (e.g., neurobiology), physiology, and psychology (affective sciences, positive psychology, cognitive psychology, psychophysiology, neuropsychology, etc.). To organize the knowledge, we employ technologies such as Artificial Intelligence with Knowledge Graphs and Semantic Reasoning. Furthermore, Internet of Things (IoT) technologies can help to acquire physiological data knowledge. The goal of this paper is to aggregate the interdisciplinary knowledge and implement it within the Emotional Knowledge Graph (EmoKG). The Emotional Knowledge Graph is used within our naturopathy recommender system that suggests food to boost emotion (e.g., chocolate contains magnesium that is recommended when we feel depressed). The recommender system also answers a set of competency questions to easily retrieve emotional related-knowledge from EmoKG, such as what are the basic emotions and the more sophisticated ones, what are the neurotransmitters and hormones related to emotions, etc. To follow FAIR principles, EmoKG is mapped to existing knowledge bases found on the BioPortal biomedical ontology catalog such as SNOMEDCT, FMA, RXNORM, MedDRA, and also from emotion ontologies (when available online). We design the LOV4IoT-Emotion ontology catalog that encourages researchers from heterogeneous communities to apply FAIR principles by releasing online their (emotion) ontologies, datasets, rules, etc. The set of ontology codes shared online can be semi-automatically processed; if not available, the scientific publications describing the emotion ontologies are semi-automatically processed with Natural Language Processing (NLP) technologies. This research is also relevant for other use cases such as European projects (ACCRA for emotional robots to reduce the social isolation of aging people, StandICT for standardization, and AI4EU for Artificial Intelligence) and alliances for IoT such as AIOTI. The recommender system can be extended to address other advice such as aromatherapy and take into consideration medical devices to monitor patients’ vital signals related to emotions and mental health.

A Possible Workflow from New and Legacy Publications to keep the World Flora Online up to date with New Species and Augmenting Taxonomic Treatments

Thousands of new species are discovered each year, and new results are published to add to the knowledge of existing species. A growing number of these are immediately accessible through the Biodiversity Literature Repository (BLR) and reused by Global Biodiversity Information Facility (GBIF), bringing the number of treatments covering plant species to over 25,000 treatments. This includes the findable, accessible, interoperable, and resuable (FAIR) treatments and related figures, and in many cases the material citation of the holotype, and links to the collection, specimen and gene sequences attribured to the codes. The FAIR data is deposited in the Biodiversity Literature Repository ensuring long-term access, and includes rich, customized metadata describing its content using standard vocabularies (e.g. Darwin Core (DwC) or Open Biological and Biomedical Ontology (OBO) Foundry, as well as links to related items and data reuse (e.g. GBIF and CheckListbank). TreatmentBank is a European Research Infrastructure, which annotates taxonomic publications including quality control, as much as possible by machine, and as needed by human curation. The process is versatile, the output can be customized and immediately used by GBIF and the Swiss Institute of Bioinformatics library system (SIBiLS) to add treatments akin to articles in PubMed Central. Another focus of conversion could be floras (several series published in Muséum national d'Histoire naturelle including those that are currently only available in print). In addition to the articles provided through these collaborations, other journals were processed, totaling 5,900 articles with 54,000 treatments of which 5,600 are plants, 48,000 figures, 140,000 material citations, of which 9,000 (670 plants) are new species and 670 (16) new genera described in 2021. Arcadia Fund is funding a project to include over 100 additional journals into the daily processing, as well as annotating data from legacy publications, with the possibility of including more journals covering plant taxonomy. The workflow is based on the open source software GoldenGate Imagine, a tool used to convert articles from PDF format into an Image Markup File (IMF) export file including all the annotations in a set of CSV files and allowing checking and improve the quality of data. The quality control parameter can be defined according to specific user requirements and the curation of the annotation can be done as part of the workflow or by third parties. The IMF can be uploaded to TreatmentBank where FAIR data is generated through their deposits in the Biodiversity Literature Repository. All the data is accessible from the Plazi API in various formats such as XML, DwC-A (DarwinCore Archive) or RDF, and checked when the data has been deposited or reused by returning the link of the respective recipient’s reuse identifier. Furthermore, the treatment citations annotated in the publications provide access to the treatments related to the same taxon, including synonyms. In this presentation, we explain the data annotated and made FAIR by TreatmentBank in order to discuss what would be needed to import this rapidly increasing corpus of taxonomic treatments as a new source for WFO (World Flora Online). A side effect of the discussion could be suggestions to publishers on how they should provide data so that the taxonomic data can be automatically integrated into WFO to keep it as up-to-date as possible.

Identification of missing hierarchical relations in the vaccine ontology using acquired term pairs

BackgroundThe Vaccine Ontology (VO) is a biomedical ontology that standardizes vaccine annotation. Errors in VO will affect a multitude of applications that it is being used in. Quality assurance of VO is imperative to ensure that it provides accurate domain knowledge to these downstream tasks. Manual review to identify and fix quality issues (such as missing hierarchical is-a relations) is challenging given the complexity of the ontology. Automated approaches are highly desirable to facilitate the quality assurance of VO.MethodsWe developed an automated lexical approach that identifies potentially missing is-a relations in VO. First, we construct two types of VO concept-pairs: (1) linked; and (2) unlinked. Each concept-pair further derives an Acquired Term Pair (ATP) based on their lexical features. If the same ATP is obtained by a linked concept-pair and an unlinked concept-pair, this is considered to indicate a potentially missing is-a relation between the unlinked pair of concepts.ResultsApplying this approach on the 1.1.192 version of VO, we were able to identify 232 potentially missing is-a relations. A manual review by a VO domain expert on a random sample of 70 potentially missing is-a relations revealed that 65 of the cases were valid missing is-a relations in VO (a precision of 92.86%).ConclusionsThe results indicate that our approach is highly effective in identifying missing is-a relation in VO.

Matching Biomedical Ontologies via a Hybrid Graph Attention Network.

Biomedical ontologies have been used extensively to formally define and organize biomedical terminologies, and these ontologies are typically manually created by biomedical experts. With more biomedical ontologies being built independently, matching them to address the problem of heterogeneity and interoperability has become a critical challenge in many biomedical applications. Existing matching methods have mostly focused on capturing features of terminological, structural, and contextual semantics in ontologies. However, these feature engineering-based techniques are not only labor-intensive but also ignore the hidden semantic relations in ontologies. In this study, we propose an alternative biomedical ontology-matching framework BioHAN via a hybrid graph attention network, and that consists of three techniques. First, we propose an effective ontology-enriching method that refines and enriches the ontologies through axioms and external resources. Subsequently, we use hyperbolic graph attention layers to encode hierarchical concepts in a unified hyperbolic space. Finally, we aggregate the features of both the direct and distant neighbors with a graph attention network. Experimental results on real-world biomedical ontologies demonstrate that BioHAN is competitive with the state-of-the-art ontology matching methods.

Modular Ontology Design: A State-of-Art of Diseases Ontology Modeling and Possible Issue

The evolution of ontological engineering leaded authors to use some techniques of software engineering to design ontologies. Are obtained from these techniques the monolithic or modularized Ontologies. When is difficult to reuse some concepts of monolithic ontologies, modularized Ontologies facilitate ontology management, understandability and reuse. This paper aims to survey on ontology modularization techniques and their contribution in biomedical ontologies design. Modularization reposed on appropriated techniques and some challenges related to ontology reused, scalable querying, collaborative authoring, and distributed reasoning. For most of disease ontologies, more especially ontologies which reused IDO, these challenges are not considered, and most of them are implemented with OWL language and the novel mode to construct ontology’s purpose is to facilitate reuse and interoperability of ontologies ensured by modularization.

BERTMap: A BERT-Based Ontology Alignment System

Ontology alignment (a.k.a ontology matching (OM)) plays a critical role in knowledge integration. Owing to the success of machine learning in many domains, it has been applied in OM. However, the existing methods, which often adopt ad-hoc feature engineering or non-contextual word embeddings, have not yet outperformed rule-based systems especially in an unsupervised setting. In this paper, we propose a novel OM system named BERTMap which can support both unsupervised and semi-supervised settings. It first predicts mappings using a classifier based on fine-tuning the contextual embedding model BERT on text semantics corpora extracted from ontologies, and then refines the mappings through extension and repair by utilizing the ontology structure and logic. Our evaluation with three alignment tasks on biomedical ontologies demonstrates that BERTMap can often perform better than the leading OM systems LogMap and AML.

Anatomy and the type concept in biology show that ontologies must be adapted to the diagnostic needs of research

BackgroundIn times of exponential data growth in the life sciences, machine-supported approaches are becoming increasingly important and with them the need for FAIR (Findable, Accessible, Interoperable, Reusable) and eScience-compliant data and metadata standards. Ontologies, with their queryable knowledge resources, play an essential role in providing these standards. Unfortunately, biomedical ontologies only provide ontological definitions that answer What is it? questions, but no method-dependent empirical recognition criteria that answer How does it look? questions. Consequently, biomedical ontologies contain knowledge of the underlying ontological nature of structural kinds, but often lack sufficient diagnostic knowledge to unambiguously determine the reference of a term.ResultsWe argue that this is because ontology terms are usually textually defined and conceived as essentialistic classes, while recognition criteria often require perception-based definitions because perception-based contents more efficiently document and communicate spatial and temporal information—a picture is worth a thousand words. Therefore, diagnostic knowledge often must be conceived as cluster classes or fuzzy sets. Using several examples from anatomy, we point out the importance of diagnostic knowledge in anatomical research and discuss the role of cluster classes and fuzzy sets as concepts of grouping needed in anatomy ontologies in addition to essentialistic classes. In this context, we evaluate the role of the biological type concept and discuss its function as a general container concept for groupings not covered by the essentialistic class concept.ConclusionsWe conclude that many recognition criteria can be conceptualized as text-based cluster classes that use terms that are in turn based on perception-based fuzzy set concepts. Finally, we point out that only if biomedical ontologies model also relevant diagnostic knowledge in addition to ontological knowledge, they will fully realize their potential and contribute even more substantially to the establishment of FAIR and eScience-compliant data and metadata standards in the life sciences.

Measuring of similarity between verb pairs in the biomedical domain

Finding the similarity Ontology-based between two verbs in bio-medical ontology is a difficult task as there is no standard dataset available. This paper is focused on verb-based similarity. So, the similarity between two nouns is taken as the benchmark for working on the verb similarity. There is no exact idea that the verb hierarchy of wordnet is capable to calculate the verb similarity between two verbs. The finding of similarity considers three parameters such as path, link, and depth. But in this paper, in addition to the path, link, and depth parameters, we also considered parameters such as stem-similarity weighting, derivation nouns weighting, and gloss similarity weighting. Moreover, we implemented two algorithms namely Rich Hierarchy Exploration and Shallow Hierarchy Exploration on a dataset and found that there is no significant difference in its performance.

A vector-based semantic relatedness measure using multiple relations within SNOMED CT and UMLS

ObjectiveTo propose a new vector-based relatedness metric that derives word vectors from the intrinsic structure of biomedical ontologies, without consulting external resources such as large-scale biomedical corpora. Materials and MethodsSNOMED CT on the mapping layer of UMLS was used as a testbed ontology. Vectors were created for every concept at the end of all semantic relations—attribute–value relations and descendants as well as is_a relation—of the defining concept. The cosine similarity between the averages of those vectors with respect to each defining concept was computed to produce a final semantic relatedness. ResultsTwo benchmark sets that include a total of 62 biomedical term pairs were used for evaluation. Spearman’s rank coefficient of the current method was 0.655, 0.744, and 0.742 with the relatedness rated by physicians, coders, and medical experts, respectively. The proposed method was comparable to a word-embedding method and outperformed path-based, information content-based, and another multiple relation-based relatedness metrics. DiscussionThe current study demonstrated that the addition of attribute relations to the is_a hierarchy of SNOMED CT better conforms to the human sense of relatedness than models based on taxonomic relations. The current approach also showed that it is robust to the design inconsistency of ontologies. ConclusionUnlike the previous vector-based approach, the current study exploited the intrinsic semantic structure of an ontology, precluding the need for external textual resources to obtain context information of defining terms. Future research is recommended to prove the validity of the current method with other biomedical ontologies.

Analysis of Causal Relationships in Integrated Ontologies of Diseases, Phenotypes, and Radiological Diagnosis.

Biomedical ontologies encode knowledge in a form that makes it computable. The current study used the integration of three large biomedical ontologies-the Disease Ontology (DO), Human Phenotype Ontology (HPO), and Radiology Gamuts Ontology (RGO)-to explore inferred causal relationships between high-level DO and HPO concepts. The principal DO categories were defined as the 7 direct subclasses of the top-level Disease class, excluding Disease of anatomical entity, plus the 12 direct subclasses of the latter term. The principal HPO categories were defined as the 25 direct subclasses of HPO's Phenotypic abnormality class. All causal relationships were tallied between members of the DO and HPO principal categories through their causal relationships in RGO. The analysis provides an understanding of the hierarchical organization of RGO terms, and offers insights into new relationships between DO and HPO classes.

Pattern-Based Logical Definitions of Prenatal Disorders Grounded on Dispositions.

Biomedical ontologies define concepts having biomedical significance and the semantic relations among them. Developing high-quality and reusable ontologies in the biomedical domain is a challenging task. Pattern-based ontology design is considered a promising approach to overcome the challenges. Ontology Design Patterns (ODPs) are reusable modeling solutions to facilitate ontology development. This study relies on ODPs to semantically enrich biomedical ontologies by assigning logical definitions to ontological entities. Specifically, pattern-based logical definitions grounded on dispositions are given to prenatal disorders. The proposed approach is performed under the supervision of fetal domain experts.