Journal Of Biomedical Semantics[JOURNAL]
Sun
200 papers
Immune biomarkers, profiles, and responses: a vaccine ontology perspective.
He Y, Huffman A, Zheng J … +3 more , Masci AM, Lin AY, Smith B
J Biomed Semantics · 2026 Jun · PMID 42374599 · Full text
BACKGROUND: Vaccines have the ability to induce a range of immune responses under different conditions, for example stimulating the production of neutralizing antibodies to block pathogen entry or activating cytotoxic T...
BACKGROUND: Vaccines have the ability to induce a range of immune responses under different conditions, for example stimulating the production of neutralizing antibodies to block pathogen entry or activating cytotoxic T cells to eliminate infected cells. Many such immune responses have not been thoroughly examined and classified. The Vaccine Ontology (VO) is a community-based ontology in the domain of vaccinology. We here describe how VO is used to represent the variety of immune responses associated with vaccines, together with associated biomarkers and profiles. RESULTS: The VO differentiates 'vaccination' and 'vaccine immunization.' The former is a process of administering a vaccine in vivo; the latter is the outcome of vaccine induction of immune response. This distinction is critical for understanding both the procedure of vaccination and the resulting immune effects. VO also models and represents various vaccine-induced responses at multiple biological levels, including population, organism, organ/tissue, cell, and gene/protein levels. Such an approach captures the complexity of vaccine-induced immunity, from population-wide trend (for example: herd immunity) to molecular mechanisms. VO defines immune biomarkers as material entities such as neutralizing antibodies that signify a humoral immune response, and IFN gamma that is indicative of cell-mediated responses. Such biomarkers provide measurable indicators of the immune system's functional state post vaccination, enabling robust evaluation of vaccine efficacy. VO classifies 'immune response profile' and 'correlated profile (or correlate) of immune protection' as 'process profiles,' a class in the Basic Formal Ontology (BFO 2.0). Immune response profiles, such as 'Th1 (or Th2)-biased profile,' can be induced by various vaccines and vaccine adjuvants. Different types of 'correlated profile of immune protection' are also identified, such as mechanistic and non-mechanistic correlates of immune protection. CONCLUSION: The important immune-related terms for immune biomarkers, profiles, and responses are modeled ontologically in VO together with their interrelations. The results support enhanced classification and analysis of vaccine-induced immune responses and related biomarkers and immune profiles, leading to further understanding of the vaccine immune mechanisms and enhanced vaccine research and development.
A pragmatist approach to bridging tables and ontologies through LinkML and punning.
Duncan WD, Mungall CJ, Guralnick RP
J Biomed Semantics · 2026 Jun · PMID 42337582 · Full text
BACKGROUND: Ontologies support data integration and knowledge discovery across multiple life science disciplines through the application of formal semantics that define hierarchical and cross-hierarchical relationships w...
BACKGROUND: Ontologies support data integration and knowledge discovery across multiple life science disciplines through the application of formal semantics that define hierarchical and cross-hierarchical relationships which enable automated reasoning capabilities. At the same time, tabular data is by far the most commonly produced format of data in most disciplines. This is especially true in biodiversity dataset production. Creating rich semantic data from tables can be complex because of a mismatch between flat tabular data and hierarchical ontology models. Here, we present a practical approach for closing this gap using Linked Data Modeling Language (LinkML) in combination with OWL punning. LinkML allows tabular schemas to be formally defined, validated, and automatically documented in combination. OWL punning allows schema elements to function simultaneously as both classes and data properties. This approach has many practical advantages that we showcase using mammal trait data assembled from the Ranges digitization network. RESULTS: Our work demonstrates how using LinkML + punning simplifies alignment of trait data in tabular format with ontologies such as the FuTRES Ontology of Vertebrate Traits (FOVT). Additionally, we show multiple advantages of our practical approach, including: maintaining synchronization between schema, ontology, and documentation, reducing modeling overhead, and preserving compatibility with conventional relational data workflows. On the downside, full logical reasoning is limited relative to more verbose RDF translations. Often this is a perfectly acceptable tradeoff for many use cases focused on data discovery and integration. CONCLUSION: The LinkML + punning framework shown here provides a scalable and pragmatic strategy for building semantically rich, ontology-aligned data resources that lower the barrier to semantic interoperability in biodiversity and trait informatics.
FAIR in practice: minimum metadata schema for bioinformatics analytics by machines.
Wijnbergen D, Queralt-Rosinach N, Barbié V … +7 more , Verkinderen E, Benis N, Jacobsen A, 't Hoen PAC, Carta C, Roos M, Mina E
J Biomed Semantics · 2026 Jun · PMID 42321841 · Full text
BACKGROUND: One pillar of FAIR principles adoption is Reusability by machines to enable for example more efficient data analytics in fields such as Bioinformatics. However, it is not clear to what extent current metadata...
BACKGROUND: One pillar of FAIR principles adoption is Reusability by machines to enable for example more efficient data analytics in fields such as Bioinformatics. However, it is not clear to what extent current metadata exposed by datasets and tools in common repositories enable this. In practice, metadata often lacks in machine actionability due to incomplete standardised metadata and lack of ontological descriptions. RESULTS: In this work, we identified minimal metadata that is needed to improve the machine actionability of bioinformatics tools and proposed a schema to address current limitations. The schema consists of metadata properties for the identification, selection, validation, and execution of tools. We also aligned this metadata to the metadata of datasets, in order to improve their integration for analytics by machines. CONCLUSIONS: The identified minimal metadata improves the machine actionability of tools and data, and can be incorporated into platforms for tool and data sharing, and in FAIR infrastructures.
Prenatal monitoring in primary health care: a design science research-based approach to FHIR interoperability.
Braga RD, Leitao-Junior PS, Castilho SB … +11 more , Souza DA, Oliveira LB, Oliveira MLD, Esmeraldo LL, Tibiriçá CAG, Guimarães DF, Nogueira AR, Vilela LM, Ribeiro-Rotta RF, Lucena FN, Souza-Zinader JP
J Biomed Semantics · 2026 May · PMID 42185933 · Full text
BACKGROUND: This research addresses the development and validation of an information model for pregnancy monitoring that, through the Fast Healthcare Interoperability Resources (FHIR) specification, seeks to promote inte...
BACKGROUND: This research addresses the development and validation of an information model for pregnancy monitoring that, through the Fast Healthcare Interoperability Resources (FHIR) specification, seeks to promote interoperability for the quality of prenatal care by multidisciplinary teams in Brazilian primary healthcare contexts. The Design Science Research (DSR) approach is applied to propose and evaluate the model, through quality strategies, where iterations increase the mode maturity in accordance with healthcare scenarios. RESULTS: A set of use cases is constructed from user stories in primary care that abstract health concepts for pregnancy monitoring. The information model is presented in a hierarchical structure, with the categorization of related concepts grouped into pillars, in the context of the integration of health professionals who work in a complementary manner. The traceability of the use cases in relation to the semantic pillars of the information model, the implementation of the model from a FHIR perspective, and how the model complies with the ISO 13972:2022 standard (Health informatics - Clinical information models - Characteristics, structures and requirements) are analyzed. CONCLUSIONS: This work makes several key contributions, starting with the development of an information model for pregnancy monitoring. This model integrates different areas of primary care to promote a holistic and personalized approach to prenatal care. To ensure data can be shared effectively, it establishes health information interoperability using the HL7 FHIR R4 standard, which involved mapping resources and creating profiles as specified in an implementation guide. The study rigorously applies the Design Science Research method, utilizing multidisciplinary consensus strategies to scientifically improve and evolve the information model.
From narrative evidence to computable knowledge: a decision-relevant corpus for medicinal herb-disease relationships.
Yea S, Jang H, Kim JU
J Biomed Semantics · 2026 May · PMID 42129926 · Full text
BACKGROUND: Traditional medicine (TM) employs medicinal herbs (MHs) to manage diverse health conditions, yet much of this evidence remains embedded in narrative biomedical text, limiting its use in computational analysis...
BACKGROUND: Traditional medicine (TM) employs medicinal herbs (MHs) to manage diverse health conditions, yet much of this evidence remains embedded in narrative biomedical text, limiting its use in computational analysis and decision making. As integrative medicine increasingly demands reliable, machine-interpretable knowledge, there is a need for structured representations of MH-disease relationships that prioritize evidential clarity and semantic consistency. This study aims to develop an expert-annotated corpus designed to support decision-relevant knowledge extraction for integrative biomedical informatics. METHODS: We developed the Medicinal Herb-Disease Relationships (MHDR) corpus by systematically collecting 800 PubMed abstracts using standardized pharmacognostic names. To ensure reliability for decision-support applications, relationships were extracted exclusively from key sentences-sentences that explicitly and unambiguously state MH-disease associations and provide direct evidence for the underlying claims. Relationships expressed in non-key sentences were intentionally excluded, as they often contain implicit, speculative, or context-dependent statements that may reduce decision reliability. Three TM experts manually annotated MH entities, disease entities, and explicit relationships through a four-phase consensus-driven protocol. Baseline Transformer-based models were evaluated for entity recognition, key-sentence identification, and relation extraction to assess the corpus's computational usability. RESULTS: The MHDR corpus contains 5,119 medicinal herb mentions, 6,621 disease mentions, and 1,314 high-confidence MH-disease relationships derived from 832 decision-relevant key sentences. Baseline Transformer models demonstrated robust performance in recognizing entities and extracting relationships, confirming that the corpus supports stable and interpretable knowledge extraction suitable for downstream decision-support and evidence-integration tasks. CONCLUSION: The MHDR corpus represents a decision-oriented informatics resource for modeling medicinal herb-disease knowledge in TM. By restricting annotations to explicit evidence-bearing sentences, the corpus enhances semantic reliability and supports computable reasoning, enabling its use in clinical research analytics, ontology alignment, and integrative decision-making systems bridging traditional and modern medicine. The MHDR corpus is publicly available through GitHub (https://github.com/KIOM-AIDoc/MHDR) and Figshare (https://doi.org/10.6084/m9.figshare.29555549).
BERTopic-driven term extraction from biomedical texts toward ontology population: evaluating vaccine ontology with Plotkin's vaccines corpus.
Jesudas BD, Smith S, Yeh FY … +4 more , Zheng J, Beverley J, Duncan WD, He Y
J Biomed Semantics · 2026 May · PMID 42071270 · Full text
BACKGROUND: Ontologies are essential for structuring biomedical knowledge, supporting semantic integration, reasoning, and data interoperability. In vaccinology, ontology population is particularly critical, as vaccines...
BACKGROUND: Ontologies are essential for structuring biomedical knowledge, supporting semantic integration, reasoning, and data interoperability. In vaccinology, ontology population is particularly critical, as vaccines span diverse domains. A well-defined Vaccine Ontology (VO) enables consistent knowledge representation, integration across datasets, and supports applications such as decision support, literature mining, and semantic search. However, manual ontology population is tedious, time-consuming, and difficult to maintain in this dynamically evolving domain, underscoring the need for automated or semi-automated population approaches. METHODS: We present a semi-automated pipeline that uses Bidirectional Encoder Representations from Transformers and Topic Modeling (BERTopic) to extract ontology-relevant concepts from biomedical text. To evaluate the effectiveness of this automated approach, the method is applied to Plotkin's Vaccines corpus, a leading reference text in vaccinology that synthesizes scientific, clinical, and policy perspectives on vaccines. The workflow integrates multiple natural language processing (NLP) components: document preprocessing with spaCy part-of-speech tagging and vectorization, sentence embeddings generated by a lightweight transformer model (all-MiniLM-L6-v2), dimensionality reduction with Uniform Manifold Approximation and Projection (UMAP), clustering with Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), and topic representation via Class-based Term Frequency - Inverse Document Frequency (c-TF-IDF). To guide topic discovery toward vaccine-relevant concepts and filter irrelevant terms, the pipeline incorporates a curated set of vaccine-focused terms derived from an existing vaccine ontology as seed words to influence topic representations, while preserving the unsupervised nature of the clustering process. To enhance interpretability, the pipeline employs Keyword extraction using BERT embeddings (KeyBERT) for automatic keyword-based labeling, supplemented with disambiguated descriptive labels, and Bidirectional and Auto-Regressive Transformer (BART) summarization for topic-level summaries. The resulting hierarchical topic structures are further refined through a tree-merging module that unifies multiple topic hierarchies into a coherent ontology-like representation. The extracted topics are reviewed by the Subject Matter Experts (SMEs) to filter irrelevant terms and then mapped to Vaccine Ontology, a well-established ontology to assess their relevance and coverage, demonstrating how automated methods can reduce the labor-intensive effort required for manual ontology population. RESULTS: The script is customized to generate a varying number of topics and keywords. In this study, the top 50 topics with 10 keywords per topic were extracted for each chapter of Plotkin's vaccines. The pipeline produced coherent topic clusters representing key themes in vaccinology, including immune mechanisms, pathogen-specific vaccines, and vaccine types. The hierarchical tree-merging process is used to illustrate how semantically related concept groupings emerge and can suggest potential ontology subdivisions. This serves as a visualization of conceptual relationships derived from the data and is particularly helpful for SMEs to review, interpret, and validate candidate concepts. CONCLUSIONS: This study demonstrates the feasibility of BERTopic-driven, a semi-automated approach for extracting ontology-relevant concepts from biomedical texts. The method was evaluated using a foundational vaccinology corpus and assessed against an existing, well-developed vaccine ontology to determine the relevance and coverage of the extracted topics. Mapping the topics to the established ontology enabled identification of concept alignments and irrelevant terms, which were subsequently reviewed by SMEs. The results show that the proposed approach can effectively surface meaningful, ontology-relevant concepts while significantly reducing the time and effort for manual population, thereby providing a scalable strategy for supporting ontology maintenance and enrichment.
ClarID: a human-readable and compact identifier specification for biomedical metadata integration.
Rueda M, Gut IG
J Biomed Semantics · 2026 Apr · PMID 42050585 · Full text
BACKGROUND: In biomedical research, subjects and biospecimens are commonly tracked using simple IDs or UUIDs, which guarantee uniqueness but convey no embedded semantic information. Contextual metadata (such as tissue ty...
BACKGROUND: In biomedical research, subjects and biospecimens are commonly tracked using simple IDs or UUIDs, which guarantee uniqueness but convey no embedded semantic information. Contextual metadata (such as tissue type, diagnosis, or assay) is often stored separately, making integration, cohort selection, and downstream analysis cumbersome. While structured barcoding systems exist in large consortia (e.g., TCGA, GTEx) or domain-specific contexts (e.g., SPREC, GOLD), no unified, extensible framework currently spans both subjects and biosamples in a human- and machine-readable way. METHODS: We developed ClarID, a domain-agnostic specification that supports two identifier formats: (i) a human-readable form (e.g., ‘CNAG_Test-HomSap-00001-LIV-TUM-RNA-C22.0-TRT-P1W’ that encodes key metadata such as project, species, subject_id, tissue, assay, disease, timepoint and duration (relative to that event); and (ii) a compact version named ‘stub’ (e.g., ‘CT01001LTR0N401T1W’) optimized for filenames, pipelines, and labeling. ClarID is supported by an open-source reference implementation, ClarID-Tools, a command-line tool that processes tabular metadata files (CSV/TSV) and uses a YAML-based codebook to generate, decode, and validate identifiers, as well as to create and read QR codes. The tool supports bulk and single-sample processing and allows easy integration with institutional workflows. RESULTS: To demonstrate ClarID’s utility, we applied it to datasets from the Genomic Data Commons (GDC), generating interpretable identifiers for more than 113,000 clinical records (subjects) and 4,255 biospecimen records. All materials, including pre-processing scripts, input and encoded data, are publicly available and fully reproducible via the accompanying GitHub repository and Google Colab. CONCLUSIONS: ClarID is designed to complement, not replace, persistent identifiers such as UUIDs, by providing a human-readable layer that enhances interpretability and facilitates metadata curation. It enhances traceability, facilitates downstream analysis, and remains adaptable to project-specific needs through a configurable codebook. The accompanying ClarID-Tools software is freely available, together with full documentation and reproducible pipelines, at https://github.com/CNAG-Biomedical-Informatics/clarid-tools .
Representing dental caries and dysbiosis within the oral microbiome in the Oral Health and Disease Ontology.
Duncan WD, Sabharwal A, Diehl AD … +4 more , Dutta N, Diller M, Joachimiak MP, Chandrasekharan GM
J Biomed Semantics · 2026 Apr · PMID 42032627 · Full text
BACKGROUND: Dental caries is an oral health condition in which cariogenic bacteria demineralize and decay teeth. It arises due to interaction between the host, environment, and oral microbiome. Current terminologies and...
BACKGROUND: Dental caries is an oral health condition in which cariogenic bacteria demineralize and decay teeth. It arises due to interaction between the host, environment, and oral microbiome. Current terminologies and ontologies, however, do not accurately represent the important role that the microbiome has in the formation of carious lesions. Rather, they focus on the anatomical features of carious lesions and often obfuscate the distinctions between dental caries as a disease affecting a tooth, as lesions that are produced because of the disease, and as lesions produced as a result of dysbiosis in the oral microbiome. To capture the current state of evidence and provide flexibility for evolving literature on host-environment-microbiome interactions, there is a need to revise and expand the ontological framework for dental caries. RESULTS: Several established terminologies and ontologies were reviewed for terms used to represent dental caries and the oral microbiome. We found that they either did not represent or misrepresented the current scientific understanding of caries and its relation to the microbial dysbiosis. As a result of these deficiencies, we added terms and relations to the Oral Health and Disease Ontology (OHD) that more accurately represent how oral microbial dysbiosis influences the development of dental caries. CONCLUSIONS: The Oral Health and Disease Ontology is an advance over existing ontologies for representing the impact of oral microbial dysbiosis on dental caries. It provides a semantic framework that better serves the needs of cariology researchers and can more easily incorporate new oral microbiome findings.
Representing dental restoration materials in the oral health and disease ontology.
Dutta N, DeBellis M, Chanda N … +6 more , Diehl AD, Wilson F, Rocha M, Diller M, Chandrasekharan GM, Duncan WD
J Biomed Semantics · 2026 Apr · PMID 41992354 · Full text
BACKGROUND: Semantic clarity and standardization in representing dental restoration materials are essential for ensuring interoperability across research and clinical settings. However, existing ontologies, including tho...
BACKGROUND: Semantic clarity and standardization in representing dental restoration materials are essential for ensuring interoperability across research and clinical settings. However, existing ontologies, including those in the Open Biological and Biomedical Ontology (OBO) Foundry, provide minimal coverage of this clinically relevant domain. METHODS: Guided by OBO Foundry principles, developed an ontological extension of the Oral Health and Disease (OHD) ontology to formally represent dental restoration materials. The modeling process included iterative expert input to ensure clinical accuracy and precise terminology. Logical definitions and subclass hierarchies were created to classify materials by composition and microstructure. RESULTS: The extended ontology introduces a logically structured taxonomy of dental restoration materials using genus-differentia definitions. Its class hierarchy aligns with domain-specific classification systems and captures current and emerging material types. Reuse of class from the Chemical Entities of Biological Interest (ChEBI) and the Environment Ontology (ENVO) as well as relations from the Relation Ontology (RO) supports semantic integration with related biomedical ontologies. CONCLUSION: This work highlights how domain-informed ontology design can effectively capture complex material knowledge. The extensive collaboration integrates expertise from ontology engineers, academic researchers, and practicing clinicians. This approach enables the capture of intricate, often-disregarded real clinical factors, resulting in a model that reflects both the structural properties of materials and their practical use. The extended OHD ontology improves the semantic representation of dental restoration materials and provides a scalable foundation for advancing oral health informatics.
Knowledge graph embedding and alignment of incomplete electronic health records for critical care applications.
Mehryar S, Dumontier M
J Biomed Semantics · 2026 Apr · PMID 41975542 · Full text
BACKGROUND: Data-driven AI models in clinical care increasingly rely on integrating data from heterogeneous sources to improve predictive performance. Traditional Electronic Health Record (EHR) systems however pose signi...
BACKGROUND: Data-driven AI models in clinical care increasingly rely on integrating data from heterogeneous sources to improve predictive performance. Traditional Electronic Health Record (EHR) systems however pose significant challenges for data integration due to the use of diverse standards and inconsistent semantics. Personalized Health Knowledge Graphs (PHKG), which leverage biomedical ontologies to encode and interconnect clinical knowledge, have emerged as a promising solution for unifying heterogeneous health data. Yet the process of PHKG integration can result in incomplete and ambiguous representations. OBJECTIVE: Our primary objective is to apply advanced embedding techniques to mitigate the incompleteness problem in PHKGs. We propose a framework that combines schema-based semantic integration, domain-specific ontology alignment, and patient context representations through embedding techniques to improve incomplete and ambiguous PHKG representations. We further demonstrate the framework’s capabilities for producing enriched representations in a use case concerning cardiovascular outcome prediction by training machine learning models which utilize the learned embeddings. RESULTS: We embed EHR from a critical care unit as structured PHKGs mapped with two different schemas for comparison across knowledge completion and alignment tasks following a modular design. Each module is individually optimized using three baseline embedding methods with enhanced loss functions. We evaluate each task for both accuracy and semantic consistency and show the contribution of each module to the overall performance. We find and report settings for each module in which the proposed framework outperforms baseline results. The learned representations are subsequently used to generate patient contexts for the task of Heart Failure diagnosis as a use case. Our experiments demonstrate that semantically enhanced PHKG embeddings achieve better precision and recall scores compared to baseline models. CONCLUSION: Our proposed method addresses the challenges in generating heterogenuous Personalized Health Knowledge Graphs (PHKG) through a modular framework that integrates schema mapping, ontology alignment, and contextual patient embeddings. Results on real-world patient records support the potential for improved performance in clinical decision making. Particularly in scenarios where sparse and fragmented health records prove problematic for data driven applications, our method can provide a robust approach for the disambiguation and completion of coded information. Our implementation is available at: https://github.com/AIDAVA-DEV/kge-framework .
Advancing the bioassay ontology through integrated PK/PD and safety pharmacology representation.
Glenny-Pescov J, Chung C, Ross N … +5 more , Hu J, Sinclair M, Khurshid R, Karlsson A, Schürer SC
J Biomed Semantics · 2026 Mar · PMID 41821121 · Full text
The latest release of the BioAssay Ontology (BAO), version 2.8.16, introduces major updates that expand its ability to describe and categorize assays related to pharmacokinetics, pharmacodynamics, and safety pharmacology...
The latest release of the BioAssay Ontology (BAO), version 2.8.16, introduces major updates that expand its ability to describe and categorize assays related to pharmacokinetics, pharmacodynamics, and safety pharmacology. These refinements, driven by collaboration with the Semantic Enrichment of Electronic Laboratory Notebook Data project, an industry initiative led by the Pistoia Alliance, address previously identified gaps in ontology coverage. New terms were added for disease models, preclinical study parameters, toxicological measurements, and detailed classifications of pharmacokinetics and pharmacodynamics assays. The update also incorporates biologically relevant target classes, including cytochrome P450 enzymes, solute carrier transporters, and uridine diphosphate-glucuronosyltransferase enzymes. Beyond content expansion, structural improvements include reassignment of terms to more specific and semantically appropriate parent classes, refinement of class dependencies, and enhanced alignment with external ontologies. Anatomical terms were reorganized to follow the Uber Anatomy Ontology hierarchy, and new chemical classes were incorporated to improve compatibility with the Chemical Entities of Biological Interest Ontology. Hundreds of additional axioms were added using existing object properties to capture assay formats, endpoints, detection methods, substrates, design strategies, and biological context. These refinements improve BAO’s semantic precision, interoperability, and reasoning capabilities. As a demonstration of these capabilities, we present a reasoning-based use case in which BAO’s equivalent class axioms enable automated classification of passive cell permeability and active efflux substrate assays. The ontology infers broader mechanistic categories from shared modeled characteristics, grouping transporter-specific and cell-based permeability assays under their mechanistic parents, while excluding assays that do not meet all restrictions. This example demonstrates its ability to support precise, inference-driven retrieval and integration of assay classes. By extending its scope and improving the clarity, consistency, and semantic depth of its classifications, BAO continues to serve as a vital resource for organizing pharmacological data and advancing research in both academic and industrial settings.
A practical and nuanced framework for entity linking evaluation.
Xu F, Nenadic G, Stevens R
J Biomed Semantics · 2026 Feb · PMID 41680815 · Full text
BACKGROUND: Entity linking maps textual mentions with entities in vocabularies. While accuracy is the primary metric for entity linking evaluation, it fails to capture the complexity of model behaviour. RESULTS: We propo...
BACKGROUND: Entity linking maps textual mentions with entities in vocabularies. While accuracy is the primary metric for entity linking evaluation, it fails to capture the complexity of model behaviour. RESULTS: We propose an entity linking evaluation framework that clarifies the target objects of metrics calculation, incorporates term hierarchy, generates performance profiles, and summarises them as model characteristics. The framework emphasises hierarchical vocabulary structures, shifting the focus from text-level label matching to semantic comparison between concepts. We illustrate the competence and utility of this framework through a case study on disease entity linking. CONCLUSION: Our results highlight the importance of aligning evaluation metrics with application-specific requirements, and provide structured hierarchical error analysis for entity linking, paving the way for more nuanced and practical assessments of entity linking systems.
An application-based ontological knowledge base of medications to support health literacy and adherence for the consumer population: an aging population use case.
Chen C, Amith M, Roberts K … +3 more , Mauldin R, Komalasari R, Tao C
J Biomed Semantics · 2026 Jan · PMID 41612400 · Full text
BACKGROUND: The geriatric population is a vulnerable population that has higher rate of chronic disease and represents the largest portion of healthcare delivered. This population is vulnerable to medication non-adherenc...
BACKGROUND: The geriatric population is a vulnerable population that has higher rate of chronic disease and represents the largest portion of healthcare delivered. This population is vulnerable to medication non-adherence. Patient medication non-adherence is a problem that can lead to increase morbidity and mortality and waste of resources. Reasons for this phenomenon are multifactorial and include poor health literacy. RESULTS: To address this issue, we created a patient-directed drug information knowledge graph using both patient-directed resources and the Vaccine Information Statement Ontology (VISO), and attempt to validate the knowledge graph with common patient questions. This knowledge graph model (Patient-centric Drug Knowledge Graph) includes a terminological size of 577 term nodes and 113 links. We also created five knowledge graphs using the Patient-centric Drug Knowledge Graph (PcDKG) framework that represent five top medications (atorvastatin, levothyroxine, lisinopril, metformin, and amlodipine) used by the geriatric population. The common patient questions were converted to SPARQL queries to assess the coverage the model. CONCLUSION: This initial development of the PcDKG is first knowledge graph that synthesizes concepts relating to drug information needs of the consumer population, specifically the geriatric population. This work also evolves the previous work of the VISO knowledge graph to cover wider range of medication knowledge for patients. PcDKG aims to be integrated in patient-directed tools to leverage its knowledge base, and future direction is to integrate PcDKG for digital health tools directed to the geriatric population. Our work is publicly available on our GitHub repository along with the five instance knowledge graph models.
Ontology development and use for cholangiocarcinoma risk factors and predictions: a term enrichment data analysis and machine learning classification.
Pengput A, Diehl AD
J Biomed Semantics · 2026 Jan · PMID 41572391 · Full text
BACKGROUND: Cholangiocarcinoma (CCA) is a critical public health problem in Thailand. The prevalence is much higher than other areas in the world. Data about CCA are stored in different data sources and standards in both...
BACKGROUND: Cholangiocarcinoma (CCA) is a critical public health problem in Thailand. The prevalence is much higher than other areas in the world. Data about CCA are stored in different data sources and standards in both research data sets and electronic health records (EHR). OBJECTIVE: This study aims to integrate and analyze CCA data from various sources to investigate risk factors and develop prediction models using the Cholangiocarcinoma Ontology (CCAO). METHODS: Datasets from Thailand were annotated with CCAO and analyzed using ontology-based term enrichment methods. We applied ontology term enrichment analysis, similar to that used with the Gene Ontology, for identifying significant risk factors for suspected CCA and patients with CCA. Our program provided a list of significant terms associated with CCA and a visualization of the ontology hierarchy with significant terms highlighted. The outputs of the term enrichment analyses have been used as the inputs to machine learning classification tasks. RESULTS: The results confirmed that indicators for CCA include dilated bile ducts, periductal fibrosis, and hepatic mass, based on ultrasound findings from several years prior. Our analysis also revealed demographic and lifestyle risk factors such as male gender, having no education, alcohol consumption, smoking, being a farmer, and having diabetes. We seeded a random forest classifier with the term enrichment results and predicted CCA patients with average 0.92 precision-recall curve score (0.023 standard deviation) with age, dilated bile ducts, periductal fibrosis, suspected CCA, and hepatic mass as the top five important features. CONCLUSIONS: These findings can be used to focus and monitor populations at risk for CCA. Expanding CCAO with molecular data related to CCA using ontology-driven term enrichment analysis and machine learning will help us to discover new hypotheses to decrease the morbidity and mortality of CCA in Thailand.
ECLed- a tool supporting the effective use of the SNOMED CT Expression Constraint Language.
Ohlsen T, Sander A, Ingenerf J
J Biomed Semantics · 2026 Jan · PMID 41495866 · Full text
BACKGROUND: The Expression Constraint Language (ECL) is a powerful query language for SNOMED CT, enabling precise semantic queries across clinical concepts. However, its complex syntax and reliance on the SNOMED CT Conce...
BACKGROUND: The Expression Constraint Language (ECL) is a powerful query language for SNOMED CT, enabling precise semantic queries across clinical concepts. However, its complex syntax and reliance on the SNOMED CT Concept Model make it difficult for non-experts to use, limiting its broader adoption in clinical research and healthcare analytics. OBJECTIVE: This work presents ECLed, a web-based tool designed to simplify access to ECL queries by abstracting the complexity of ECL syntax and the SNOMED CT Concept Model. ECLed is aimed at non-technical users, enabling the creation and modification of ECL queries and facilitating the querying of patient data coded with SNOMED CT. METHODS: ECLed was developed following a detailed requirements analysis, addressing both functional and non-functional needs. The tool supports the creation and editing of SNOMED CT ECL queries, integrates a processed Concept Model, and uses FHIR terminology services for semantic validation. Its modular architecture, with a frontend based on Angular and a backend on Spring Boot, ensures seamless communication through RESTful interfaces. RESULT: ECLed demonstrated high usability in a user survey. Technical validation confirmed that it reliably generates and edits complex ECL queries. The tool was successfully integrated into the DaWiMed research platform, enhancing clinical analysis workflows. It also worked effectively with clinical data in FHIR format, although scalability with larger datasets remains to be tested. DISCUSSION: ECLed overcomes the limitations of existing ECL tools by abstracting the complexity of both the syntax and the SNOMED CT Concept Model. It provides a user-friendly solution that enables both technical and non-technical users to easily create and edit ECL queries. CONCLUSION: ECLed offers a practical, user-friendly solution for creating SNOMED CT ECL queries, effectively hiding the underlying complexity while optimizing clinical research and data analysis workflows. It holds significant potential for further development and integration into additional research platforms.
Annotating and indexing scientific articles with rare diseases.
Azarbonyad H, Afzal Z, Iping R … +4 more , Dumoulin M, Nederveen I, Yu J, Tsatsaronis G
J Biomed Semantics · 2026 Jan · PMID 41495851 · Full text
BACKGROUND: Around 30 million people in Europe are affected by a rare (or orphan) disease, defined as a condition occurring in fewer than 1 in 2,000 individuals. The primary challenge is to automatically and efficiently...
BACKGROUND: Around 30 million people in Europe are affected by a rare (or orphan) disease, defined as a condition occurring in fewer than 1 in 2,000 individuals. The primary challenge is to automatically and efficiently identify scientific articles and guidelines that address a particular rare disease. We present a novel methodology to annotate and index scientific text with taxonomical concepts describing rare diseases from the OrphaNet taxonomy. This task is complicated by several technical challenges, including the lack of sufficiently large, human-annotated datasets for supervised training and the polysemy/synonymy and surface-form variation of rare disease names, which can hinder any annotation engine. RESULTS: We introduce a framework that operationalizes OrphaNet for large-scale literature annotation by integrating the TERMite engine with curated synonym expansion, label normalization (including deprecated/renamed concepts), and fuzzy matching. On benchmark datasets, the approach achieves precision = 92%, recall = 75%, and F1 = 83%, outperforming an string-matching baseline. Applying the pipeline to Scopus produces disease-specific corpora suitable for bibliometric and scientometric analyses (e.g., institution, country, and subject-area profiles). These outputs power the Rare Diseases Monitor dashboard for exploring national and global research activity. CONCLUSION: To our knowledge, this is the first systematic, scalable semantic framework for annotating and indexing rare disease literature at scale. By operationalizing OrphaNet in an automated, reproducible pipeline and addressing data scarcity and lexical variability, the work advances biomedical semantics for rare diseases and enables disease-centric monitoring, evaluation, and discovery across the research landscape.
SimSUM - simulated benchmark with structured and unstructured medical records.
Rabaey P, Heytens S, Demeester T
J Biomed Semantics · 2025 Dec · PMID 41413824 · Full text
BACKGROUND: Clinical information extraction, which involves structuring clinical concepts from unstructured medical text, remains a challenging problem that could benefit from the inclusion of tabular background informat...
BACKGROUND: Clinical information extraction, which involves structuring clinical concepts from unstructured medical text, remains a challenging problem that could benefit from the inclusion of tabular background information available in electronic health records. Existing open-source datasets lack explicit links between structured features and clinical concepts in the text, motivating the need for a new research dataset. METHODS: We introduce SimSUM a benchmark dataset of 10,000 simulated patient records that link unstructured clinical notes with structured background variables. Each record simulates a patient encounter in the domain of respiratory diseases and includes tabular data (e.g., symptoms, diagnoses, underlying conditions) generated from a Bayesian network whose structure and parameters are defined by domain experts. A large language model (GPT-4o) is prompted to generate a clinical note describing the encounter, including symptoms and relevant context. These notes are annotated with span-level symptom mentions. We conduct an expert evaluation to assess note quality and run baseline predictive models on both the tabular and textual data. CONCLUSION: The SimSUM dataset is primarily designed to support research on clinical information extraction in the presence of tabular background variables, which can be linked through domain knowledge to concepts of interest to be extracted from the text—namely, symptoms in the case of SimSUM. Secondary uses include research on the automation of clinical reasoning over both tabular data and text, causal effect estimation in the presence of tabular and/or textual confounders, and multi-modal synthetic data generation. SimSUM is not intended for training clinical decision support systems or production-grade models, but rather to facilitate reproducible research in a simplified and controlled setting. The dataset is available at https://github.com/prabaey/SimSUM .
BabelFSH-a toolkit for an effective HL7 FHIR-based terminology provision.
Wiedekopf J, Ohlsen T, Kock-Schoppenhauer AK … +1 more , Ingenerf J
J Biomed Semantics · 2025 Nov · PMID 41318591 · Full text
BACKGROUND: HL7 FHIR terminological services (TS) are a valuable tool towards better healthcare interoperability, but require representations of terminologies using FHIR resources to provide their services. As most termi...
BACKGROUND: HL7 FHIR terminological services (TS) are a valuable tool towards better healthcare interoperability, but require representations of terminologies using FHIR resources to provide their services. As most terminologies are not natively distributed using FHIR resources, converters are needed. Large-scale FHIR projects, especially those with a national or even an international scope, define enormous numbers of value sets and reference many large and complex code systems, which must be regularly updated in TS and other systems. This necessitates a flexible, scalable and efficient provision of these artifacts. This work aims to develop a comprehensive, extensible and accessible toolkit for FHIR terminology conversion, making it possible for terminology authors, FHIR profilers and other actors to provide standardized TS for large-scale terminological artifacts. IMPLEMENTATION: Based on the prevalent HL7 FHIR Shorthand (FSH) specification, a converter toolkit, called BabelFSH, was created that utilizes an adaptable plugin architecture to separate the definition of content from that of the needed declarative metadata. The development process was guided by formalized design goals. RESULTS: All eight design goals were addressed by BabelFSH. Validation of the systems' performance and completeness was exemplarily demonstrated using Alpha-ID-SE, an important terminology used for diagnosis coding especially of rare diseases within Germany. The tool is now used extensively within the content delivery pipeline for a central FHIR TS with a national scope within the German Medical Informatics Initiative and Network University Medicine and demonstrates adequate usability for FHIR developers. DISCUSSION: The first development focus was geared towards the requirements of the central research FHIR TS for the federated FHIR infrastructure in Germany, and has proven to be very useful towards that goal. Opportunities for further improvement were identified in the validation process especially, as the validation messages are currently imprecise at times. The design of the application lends itself to the implementation of further use cases, such as direct connectivity to legacy systems for catalog conversion to FHIR. CONCLUSIONS: The developed BabelFSH tool is a novel, powerful and open-source approach to making heterogenous sources of terminological knowledge accessible as FHIR resources, thus aiding semantic interoperability in healthcare in general.
The CLEAR Principle: organizing data and metadata into semantically meaningful types of FAIR Digital Objects to increase their human explorability and cognitive interoperability.
Vogt L
J Biomed Semantics · 2025 Oct · PMID 41152932 · Full text
BACKGROUND: Ensuring the FAIRness (Findable, Accessible, Interoperable, Reusable) of data and metadata is an important goal in both research and industry. Knowledge graphs and ontologies have been central in achieving th...
BACKGROUND: Ensuring the FAIRness (Findable, Accessible, Interoperable, Reusable) of data and metadata is an important goal in both research and industry. Knowledge graphs and ontologies have been central in achieving this goal, with interoperability of data and metadata receiving much attention. This paper argues that the emphasis on machine-actionability has overshadowed the essential need for human-actionability of data and metadata, and provides three examples that describe the lack of human-actionability within knowledge graphs. RESULTS: The paper propagates the incorporation of cognitive interoperability as another vital layer within the European Open Science Cloud Interoperability Framework and discusses the relation between human explorability of data and metadata and their cognitive interoperability. It suggests adding the CLEAR Principle to support the cognitive interoperability and human contextual explorability of data and metadata. The subsequent sections present the concept of semantic units, elucidating their important role in attaining CLEAR. Semantic units structure a knowledge graph into identifiable and semantically meaningful subgraphs, each represented with its own resource that constitutes a FAIR Digital Object (FDO) and that instantiates a corresponding FDO class. Various categories of FDOs are distinguished. Each semantic unit can be displayed in a user interface either as a mind-map-like graph or as natural language text. CONCLUSIONS: Semantic units organize knowledge graphs into levels of representational granularity, distinct granularity trees, and diverse frames of reference. This organization supports the cognitive interoperability of data and metadata and facilitates their contextual explorability by humans. The development of innovative user interfaces enabled by FDOs that are based on semantic units would empower users to access, navigate, and explore information in CLEAR knowledge graphs with optimized efficiency.
Three-layered semantic framework for public health intelligence.
Guru Rao S, Rokkam P, Zhang B … +6 more , Sargsyan A, Kaladharan A, Sethumadhavan P, Jacobs M, Hofmann-Apitius M, Tom Kodamullil A
J Biomed Semantics · 2025 Sep · PMID 40954476 · Full text
BACKGROUND: Disease surveillance systems play a crucial role in monitoring and preventing infectious diseases. However, the current landscape, primarily focused on fragmented health data, poses challenges to contextual u...
BACKGROUND: Disease surveillance systems play a crucial role in monitoring and preventing infectious diseases. However, the current landscape, primarily focused on fragmented health data, poses challenges to contextual understanding and decision-making. This paper addresses this issue by proposing a semantic framework using ontologies to provide a unified data representation for seamless integration. The paper demonstrates the effectiveness of this approach using a case study of a COVID-19 incident at a football game in Italy. METHOD: In this study, we undertook a comprehensive approach to gather and analyze data for the development of ontologies within the realm of pandemic intelligence. Multiple ontologies were meticulously crafted to cater to different domains related to pandemic intelligence, such as healthcare systems, mass gatherings, travel, and diseases. The ontologies were classified into top-level, domain, and application layers. This classification facilitated the development of a three-layered architecture, promoting reusability, and consistency in knowledge representation, and serving as the backbone of our semantic framework. RESULT: Through the utilization of our semantic framework, we accomplished semantic enrichment of both structured and unstructured data. The integration of data from diverse sources involved mapping to ontology concepts, leading to the creation and storage of RDF triples in the triple store. This process resulted in the construction of linked data, ultimately enhancing the discoverability and accessibility of valuable insights. Furthermore, our anomaly detection algorithm effectively leveraged knowledge graphs extracted from the triple store, employing semantic relationships to discern patterns and anomalies within the data. Notably, this capability was exemplified by the identification of correlations between a football game and a COVID-19 event occurring at the same location and time. CONCLUSION: The framework showcased its capability to address intricate, multi-domain queries and support diverse levels of detail. Additionally, it demonstrated proficiency in data analysis and visualization, generating graphs that depict patterns and trends; however, challenges related to ontology maintenance, alignment, and mapping must be addressed for the approach's optimal utilization.
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