Web Ontology Language (OWL) is designed to represent varied knowledge about things and the relationships of things. It is widely used to express complex models and address information heterogeneity of specific domains, such as underwater environments and robots. With the help of OWL, heterogeneous underwater robots are able to cooperate with each other by exchanging information with the same meaning and robot operators can organize the coordination easier. However, OWL has expressivity limitations on representing general rules, especially the statement "If … Then … Else …". Fortunately, the Semantic Web Rule Language (SWRL) has strong rule representation capabilities. In this paper, we propose a rule-based reasoner for inferring and providing query services based on OWL and SWRL. SWRL rules are directly inserted into the ontologies by several steps of model transformations instead of using a specific editor. In the verification experiments, the SWRL rules were successfully and efficiently inserted into the OWL-based ontologies, obtaining completely correct query results. This rule-based reasoner is a promising approach to increase the inference capability of ontology-based models and it achieves significant contributions when semantic queries are done.
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http://dx.doi.org/10.3390/s18103481 | DOI Listing |
J Clin Med
December 2024
Department of Rheumatology, Division of Internal Medicine, Ankara Bilkent City Hospital, Ankara 06800, Turkey.
The integration of artificial intelligence (AI) in medicine has progressed from rule-based systems to advanced models and is showing potential in clinical decision-making. In this study, the psychological impact of AI collaboration in clinical practice is assessed, highlighting its role as a support tool for medical residents. This study aimed to compare clinical decision-making approaches of junior rheumatology residents with both trained and untrained AI models in clinical reasoning, pre-diagnosis, first-line, and second-line management stages.
View Article and Find Full Text PDFPac Symp Biocomput
December 2024
Department of Computer Science, National Yang Ming Chiao Tung University Hsinchu, 300093, Taiwan, ROC, Taiwan.
The primary challenge in reporting cancer cases lies in the labor-intensive and time-consuming process of manually reviewing numerous reports. Current methods predominantly rely on rule-based approaches or custom-supervised learning models, which predict diagnostic codes based on a single pathology report per patient. Although these methods show promising evaluation results, their biased outcomes in controlled settings may hinder adaption to real-world reporting workflows.
View Article and Find Full Text PDFFront Neurol
September 2024
Department of Neurology, International Headache Center, Chinese PLA General Hospital, Beijing, China.
Objective: To determine whether the diagnostic ability of the newly designed hierarchical fuzzy diagnosis method is consistent with that of headache experts for probable migraine (PM) and probable tension-type headache (PTTH).
Background: Clinical decision support systems (CDSS) are computer systems designed to help doctors to make clinician decisions by information technology, and have proven to be effective in improving headache diagnosis by making medical knowledge readily available to users in some studies. However, one serious drawback is that the CDSS lacks the ability to deal with some fuzzy boundaries of the headache features utilized in diagnostic criteria, which might be caused by patients' recall bias and subjective bias.
J Biomed Semantics
August 2024
Department of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, FL, 32224, USA.
Background: Vaccines have revolutionized public health by providing protection against infectious diseases. They stimulate the immune system and generate memory cells to defend against targeted diseases. Clinical trials evaluate vaccine performance, including dosage, administration routes, and potential side effects.
View Article and Find Full Text PDFJMIR Med Inform
August 2024
School of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China.
Background: Knowledge graphs (KGs) can integrate domain knowledge into a traditional Chinese medicine (TCM) intelligent syndrome differentiation model. However, the quality of current KGs in the TCM domain varies greatly, related to the lack of knowledge graph completion (KGC) and evaluation methods.
Objective: This study aims to investigate KGC and evaluation methods tailored for TCM domain knowledge.
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