OBJECTIVE: Clinical practice guidelines (CPGs) for Integrated Traditional Chinese and Western Medicine (TCM and WM) are important medical documents used to assist medical decision-making and are of great significance for standardizing clinical pathways. However, due to the constraints of text format, it is difficult for Integrated TCM and WM CPGs to play a real role in medical practice. In addition, how to standardize the structure and semantic relationships between Integrated TCM and WM CPG knowledge, and realize the construction of computable, sharable and reliable CPGs, remains an urgent issue to be addressed. Therefore, we are proposing an ontology of CPGs for Integrated TCM and WM.
METHODS: We first initialized domain concepts and relationships to ensure the accuracy of the ontology knowledge structure. We then screened CPGs that meet the standards for Integrated TCM and WM, analyzed and classified the contents, and extracted the common structures. Based on the seven-step ontology construction method combined with inference-complement, referring to the representation methods and hierarchical relationships of terms and concepts in MeSH, ICD-10, SNOMED-CT, and other ontologies and terminology sets, we formed the concept structure and semantic relationship tables for the ontology. We also achieved the matching and mapping between the ontology and reference ontologies and term sets. Next, we defined the aspects and constraints of properties, selected multiple Integrated TCM and WM CPGs as instances to populate, and used ontology reasoning tools and formulated defined inference rules to reason and extend the ontology. Finally, we evaluated the performance of the ontology.
RESULTS: The content of the Integrated TCM and WM CPGs is divided into nine parts: basic information, background, development method, clinical question, recommendation, evidence, conclusion, result, and reason for recommendations. The Integrated TCM and WM CPG ontology has 152 classes and defines 90 object properties and 114 data properties, with a maximum classification depth of 4 layers. The terms of disease, drug and examination item names in the ontology have been standardized.
CONCLUSIONS: This study proposes an Integrated TCM and WM CPG ontology. The ontology adopts a modular design, which has both sharing and scaling ability, and can express rich guideline knowledge. It provides important support for the semantic processing and computational application of guideline documents.

Technology remains enmeshed in our daily lives and given its continuing presence in clinical practice and rapid technological proliferation; it becomes relevant for nurses to examine techno-onto-epistemology in relation to the discipline of nursing. This is critical considering the intersection of technology and nursing remains an area of ongoing discussion revealing a need for further philosophical reflection. To this end, this paper sought to examine the philosophy of technology from the engineering and humanities perspectives to contribute to the discussion regarding its intersection with the onto-epistemology of nursing. Although technology seems to be constantly present in nursing practice, two opposing perspectives reflecting a love-hate relationship is highlighted: technological optimism (promotes technology) and technological romanticism (dissuades technology). Based on Mitcham\'s interpretation of \'mutual relationship\' and \'being-with\', a potential way to break away from the binary perspectives is to view the intersection of/relationship between technology and nursing as being on a continuum rather than entirely monolithic entities. Caring is presented as multidimensional reflecting actions and attitudes. Arguably, some caring actions may intersect with the engineering perspective to suggest that technology can support nurses in their roles, that is, by imitating some of what nurses do, but not to replace them. From the humanities perspective, technology is presented as a way of being with humans exercising control over what technology has to offer. Put together, it is clearly time to break away from the love-hate relationship between nursing and technology. Although this emphasises a great need to build the technological competency of nurses, there is an even greater call for nurses to reflect on and voice the epistemological, ontological, axiological, and ethical issues that the application of technology raises for the discipline.

BACKGROUND: The medical knowledge graph provides explainable decision support, helping clinicians with prompt diagnosis and treatment suggestions. However, in real-world clinical practice, patients visit different hospitals seeking various medical services, resulting in fragmented patient data across hospitals. With data security issues, data fragmentation limits the application of knowledge graphs because single-hospital data cannot provide complete evidence for generating precise decision support and comprehensive explanations. It is important to study new methods for knowledge graph systems to integrate into multicenter, information-sensitive medical environments, using fragmented patient records for decision support while maintaining data privacy and security.
OBJECTIVE: This study aims to propose an electronic health record (EHR)-oriented knowledge graph system for collaborative reasoning with multicenter fragmented patient medical data, all the while preserving data privacy.
METHODS: The study introduced an EHR knowledge graph framework and a novel collaborative reasoning process for utilizing multicenter fragmented information. The system was deployed in each hospital and used a unified semantic structure and Observational Medical Outcomes Partnership (OMOP) vocabulary to standardize the local EHR data set. The system transforms local EHR data into semantic formats and performs semantic reasoning to generate intermediate reasoning findings. The generated intermediate findings used hypernym concepts to isolate original medical data. The intermediate findings and hash-encrypted patient identities were synchronized through a blockchain network. The multicenter intermediate findings were collaborated for final reasoning and clinical decision support without gathering original EHR data.
RESULTS: The system underwent evaluation through an application study involving the utilization of multicenter fragmented EHR data to alert non-nephrology clinicians about overlooked patients with chronic kidney disease (CKD). The study covered 1185 patients in nonnephrology departments from 3 hospitals. The patients visited at least two of the hospitals. Of these, 124 patients were identified as meeting CKD diagnosis criteria through collaborative reasoning using multicenter EHR data, whereas the data from individual hospitals alone could not facilitate the identification of CKD in these patients. The assessment by clinicians indicated that 78/91 (86%) patients were CKD positive.
CONCLUSIONS: The proposed system was able to effectively utilize multicenter fragmented EHR data for clinical application. The application study showed the clinical benefits of the system with prompt and comprehensive decision support.

Digital Calibration Certificates (DCCs) are a key focus in metrology digitalization, necessitating that they satisfy the criteria for machine readability and understandability. Current DCCs are machine-readable, but they are still missing the essential semantic information required for machine understandability. This shortfall is particularly notable in the lack of a dedicated semantic ontology for measurement terminologies. This paper proposes a domain ontology for measurement terminologies named the OMT (Ontology for Measurement Terminology), using a foundation of metrological terms from standards like the International Vocabulary of Metrology (VIM), the Guide to the Expression of Uncertainty in Measurement (GUM), and JJF1001. It also incorporates insights from models such as the SI Reference Point, the Simple Knowledge Organization System (SKOS), and the DCC Schema. The methodology was guided by Stanford\'s Seven-Step Method, ensuring a systematic development process tailored to the needs of metrological semantics. Through semantic expression capability verification and SPARQL query validations, the OMT has been confirmed to possess essential machine readability and understandability features. It has been successfully integrated into version 3.2.1 of DCCs across ten representative domains. This integration demonstrates an effective method for ensuring that DCCs are machine-readable and capable of interoperating within digital environments, thereby advancing the research in metrology digitization.

UNASSIGNED: In recent years, the perceptual capabilities of robots have been significantly enhanced. However, the task execution of the robots still lacks adaptive capabilities in unstructured and dynamic environments.
UNASSIGNED: In this paper, we propose an ontology based autonomous robot task processing framework (ARTProF), to improve the robot\'s adaptability within unstructured and dynamic environments. ARTProF unifies ontological knowledge representation, reasoning, and autonomous task planning and execution into a single framework. The interface between the knowledge base and neural network-based object detection is first introduced in ARTProF to improve the robot\'s perception capabilities. A knowledge-driven manipulation operator based on Robot Operating System (ROS) is then designed to facilitate the interaction between the knowledge base and the robot\'s primitive actions. Additionally, an operation similarity model is proposed to endow the robot with the ability to generalize to novel objects. Finally, a dynamic task planning algorithm, leveraging ontological knowledge, equips the robot with adaptability to execute tasks in unstructured and dynamic environments.
UNASSIGNED: Experimental results on real-world scenarios and simulations demonstrate the effectiveness and efficiency of the proposed ARTProF framework.
UNASSIGNED: In future work, we will focus on refining the ARTProF framework by integrating neurosymbolic inference.

BACKGROUND: The effective management of epilepsy in women of child-bearing age necessitates a concerted effort from multidisciplinary teams. Nevertheless, there exists an inadequacy in the seamless exchange of knowledge among healthcare providers within this context. Consequently, it is imperative to enhance the availability of informatics resources and the development of decision support tools to address this issue comprehensively.
METHODS: The development of the Women with Epilepsy of Child-Bearing Age Ontology (WWECA) adhered to established ontology construction principles. The ontology\'s scope and universal terminology were initially established by the development team and subsequently subjected to external evaluation through a rapid Delphi consensus exercise involving domain experts. Additional entities and attribute annotation data were sourced from authoritative guideline documents and specialized terminology databases within the respective field. Furthermore, the ontology has played a pivotal role in steering the creation of an online question-and-answer system, which is actively employed and assessed by a diverse group of multidisciplinary healthcare providers.
RESULTS: WWECA successfully integrated a total of 609 entities encompassing various facets related to the diagnosis and medication for women of child-bearing age afflicted with epilepsy. The ontology exhibited a maximum depth of 8 within its hierarchical structure. Each of these entities featured three fundamental attributes, namely Chinese labels, definitions, and synonyms. The evaluation of WWECA involved 35 experts from 10 different hospitals across China, resulting in a favorable consensus among the experts. Furthermore, the ontology-driven online question and answer system underwent evaluation by a panel of 10 experts, including neurologists, obstetricians, and gynecologists. This evaluation yielded an average rating of 4.2, signifying a positive reception and endorsement of the system\'s utility and effectiveness.
CONCLUSIONS: Our ontology and the associated online question and answer system hold the potential to serve as a scalable assistant for healthcare providers engaged in the management of women with epilepsy (WWE). In the future, this developmental framework has the potential for broader application in the context of long-term management of more intricate chronic health conditions.

Disease ontologies facilitate the semantic organization and representation of domain-specific knowledge. In the case of prostate cancer (PCa), large volumes of research results and clinical data have been accumulated and needed to be standardized for sharing and translational researches. A formal representation of PCa-associated knowledge will be essential to the diverse data standardization, data sharing and the future knowledge graph extraction, deep phenotyping and explainable artificial intelligence developing. In this study, we constructed an updated PCa ontology (PCAO2) based on the ontology development life cycle. An online information retrieval system was designed to ensure the usability of the ontology. The PCAO2 with a subclass-based taxonomic hierarchy covers the major biomedical concepts for PCa-associated genotypic, phenotypic and lifestyle data. The current version of the PCAO2 contains 633 concepts organized under three biomedical viewpoints, namely, epidemiology, diagnosis and treatment. These concepts are enriched by the addition of definition, synonym, relationship and reference. For the precision diagnosis and treatment, the PCa-associated genes and lifestyles are integrated in the viewpoint of epidemiological aspects of PCa. PCAO2 provides a standardized and systematized semantic framework for studying large amounts of heterogeneous PCa data and knowledge, which can be further, edited and enriched by the scientific community. The PCAO2 is freely available at https://bioportal.bioontology.org/ontologies/PCAO, http://pcaontology.net/ and http://pcaontology.net/mobile/.

A Personal Health Knowledge Graph (PHKG) facilitates the efficient integration of potential diagnostic clues from patients\' electronic health records with medical knowledge, establishing diagnostic reasoning paths and ensuring accurate, individually interpretable results in the diagnosis of pelvic masses.

BACKGROUND: Biological research is generating high volumes of data distributed across various sources. The inconsistent naming of proteins and their encoding genes brings great challenges to protein data integration: proteins and their coding genes usually have multiple related names and notations, which are difficult to match absolutely; the nomenclature of genes and proteins is complex and varies from species to species; some less studied species have no nomenclature of genes and proteins; The annotation of the same protein/gene varies greatly in different databases. In summary, a comprehensive set of protein/gene synonyms is necessary for relevant studies.
RESULTS: In this study, we propose an approach for protein and its encoding gene synonym integration based on protein ontology. The workflow of protein and gene synonym integration is composed of three modules: data acquisition, entity and attribute alignment, attribute integration and deduplication. Finally, the integrated synonym set of proteins and their coding genes contains over 128.59 million terminologies covering 560,275 proteins/genes and 13,781 species. As the semantic basis, the comprehensive synonym set was used to develop a data platform to provide one-stop data retrieval without considering the diversity of protein nomenclature and species.
CONCLUSIONS: The synonym set constructed here can serve as an important resource for biological named entity identification, text mining and information retrieval without name ambiguity, especially synonyms associated with well-defined species categories can help to study the evolutionary relationships between species at the molecular level. More importantly, the comprehensive synonyms set is the semantic basis for our subsequent studies on Protein-protein Interaction (PPI) knowledge graph.

Objective To develop a traceable cancer hallmark ontology with terminology including gene mutation,cancer hallmark,and cell line for knowledge integration,standardization,correlation,and discovery.Methods The Ontology Development 101 and the current ontology development methods were employed to determine the content coverage,structural layers,reusable terms,and new terms of the cancer hallmark ontology.Taking colorectal cancer as a study case,we extracted the knowledge related with colorectal cancer hallmarks using text mining and text classification technology from PubMed,and then formalized the extracted knowledge into the cancer hallmark ontology.Moreover,we made use of existing cancer hallmark evidence in Catalogue of Somatic Mutations in Cancer and further semantic retrieval to discover new knowledge.Results The established cancer hallmark ontology comprised 9910 classes and 6138 instances,which realized the semantic representation of 2310 article abstracts about colorectal cancer and 26 pieces of evidence about genes and their cancer hallmarks.Compared with the Catalogue of Somatic Mutations in Cancer,new evidence for more genes associated with colorectal cancer hallmarks was found based on cancer hallmark ontology.Conclusion This study is of great significance to the research on the cancer pathogenesis at the molecular level,the revealing of specific roles of genes and mutations in the occurrence of cancer,and the rapid knowledge discovery of cancer hallmarks.
目的 构建一种可溯源的癌症标志知识本体,支持基因突变、癌症标志和细胞系等相关数据整合、标准化和关联,并基于该本体实现新知识发现。方法 基于“七步法”和现阶段本体的构建方法,明确癌症标志本体覆盖范围、框架结构、可复用的术语和新增术语;以结直肠癌为例,利用文本挖掘和文本分类技术对PubMed文献中结直肠癌标志相关知识进行提取,利用癌症标志本体对提取的知识进行规范化表达。结合癌症体细胞突变目录中已有的癌症标志证据和进一步语义检索,实现新知识的发现。结果 最终构建癌症标志本体包含类9910个,实例6138个,实现2310篇涉及结直肠癌标志文献摘要和26个结直肠癌标志相关证据的语义表示。与癌症体细胞突变目录数据集相比,本研究发现与结直肠癌相关的多个基因的癌症标志的新证据。结论 本研究有助于整合癌症分子层面的致病机制,了解基因突变在癌症发生过程中的具体作用,实现癌症标志知识的快速发现。.