Halal tourism is a fast-growing market. However, information about halal services, certifications, and facilities is scattered across many sources. Existing tourism ontologies model general tourism concepts such as destinations, activities, and accommodations, but they do not represent Islamic requirements such as prayer facilities, halal certification, or dietary compliance. Manual ontology development is slow. Large language models (LLMs) can speed up concept extraction and hierarchy proposal, but they cannot decide religious or cultural matters. In this article, we present SAFAR-Onto, an OWL 2 DL ontology for halal tourism. We develop it through a hybrid AI–human process across four phases: knowledge acquisition, conceptualization, formalization, and dual validation. The process adapts METHONTOLOGY and Simple Knowledge Engineering. LLMs assist with concept extraction and dimensional analysis. An Islamic studies validation gate routes culturally sensitive decisions to human experts. We evaluate the ontology with two methods: expert review across five quality dimensions, and the OQuaRE technical metrics. SAFAR-Onto contains 10 main classes, 75 subclasses, 201 object properties, and 200 data properties, built from 67 validated domain concepts. Six experts from tourism management, computer science, and Islamic studies rated the ontology between 4.0 and 4.5 on a five-point Likert scale, with accuracy highest at 4.5. Fourteen of 18 OQuaRE metrics scored Excellent or Very Good. We also report a verified mapping from expert feedback to ontology revisions. The ontology is publicly available at https://w3id.org/def/SAFAROnto under a CC-BY-4.0 license. We hypothesize that the development process can transfer to adjacent culturally specific domains such as Islamic finance and kosher food systems.
Physiotherapists need appropriate data management for patient management with disabilities. Rehabilitation principles, such as evaluation, goal setting, treatment, and results, are the framework for therapy development. Nevertheless, the suitability of controlled rehabilitation studies for all instances of real-life procedures has been an issue that still requires attention because of the differences in the disease conditions that patients may have. In Palestine, such rehabilitation data is often very unstructured, semi-structured, and manually logged, which makes integration within the patient's medical report a challenging task. The existing rehabilitation ontologies, in the form of physical medicine and rehabilitation, do not completely support standard physical therapy (PT) and occupational therapy (OT) assessments as used in Palestine. This is because their combination with the ontology was not applied. To address this problem, a new rehabilitation assessment ontology (RAO) system is suggested as the foundation, which will be composed of PT and OT standard assessments alongside vital patient information. The RAO was developed using the 101-ontology method by conducting interviews across the three rehabilitation centers in Palestine: Specialized Al-Ibtisameh Al-Jamelah Physiotherapy Center (BJC), Hashmi for Specialized Physiotherapy and Spinal Manipulation (HSPT), and Future Magic. The knowledge was collected from a patient profile containing a total of 120 and was utilized to create automatic ontology population rehab gazetteers. The RAO was assessed by a group of medical and ontology specialists, who explained that it was completed along the dimensions of accuracy, extensibility, usability, and completeness. This small scale accurately answered 36 well-defined competency questions. Among others, the RAO solution system presents a structured, interoperable, and improved rehabilitation data organization, simplifying queries to bring about better patient care in the three centers.
Combined with machine learning techniques, ontologies have recently been applied widely to enhance decision-making, reasoning, knowledge management and representation, and information retrieval in various domains. Likewise, the popularity of ontologies is increasing in the rapidly evolving field of precision agriculture due to the ability to encode general domain knowledge and specialized knowledge of farmers and utilize it within data processing and analytics pipelines. Although the research on ontologies applied to precision agriculture problems is increasing, adopting published solutions is rather cumbersome, mainly due to low reproducibility of these works. This article is intended to survey the current applied research in ontologies within the agricultural domain, by investigating the used methodologies, technologies and evaluation approaches, the availability of the data, ontologies and source codes developed. It was found that the used data sources, the developed ontologies and the developed source codes are available only in 14%, 9% and 7%, respectively, of the selected 85 articles. Additionally, it was found that the applied methodologies, in relation to the issue of reproducibility, the top-down/bottom-up, semi-automatic, seven-step and ontology matching approaches have shown greater potential, while according to the applied technologies, World Wide Web (W3C) have shown greater contribution compared to artificial intelligence (AI).
The field of computational complexity theory is a core theoretical subject in computer science with significant impact also for real-world applications. Although a plethora of individual results are known, a systematic conceptual organisation of this knowledge is still lacking. We propose a modelling approach for creating an ontologically well-founded knowledge base for the theory of computational complexity that will enable storing, querying and reasoning over the vast knowledge of algorithmic problems, complexity classes and their relationships. We determine the core concepts and relations of complexity theory and model them on two levels of approximation: a lightweight version based on the decidable description logic (the underlying formalism of the ontology language OWL 2 DL) and a further extended version based on first-order logic.
Precision beekeeping is an emerging domain that utilizes digital technologies and tools, including sensors and artificial intelligence, to enhance the management and sustainability of apiaries. However, the lack of formalized and structured knowledge representation models hinders data integration, interoperability and decision support in the domain. This work presents a precision beekeeping ontology (PBO) designed to represent concepts related to bees, smart devices, monitoring and observation. The ontology was developed by adopting the seven-step methodology using Prot & eacute;g & eacute;. Domain understanding and determining the scope of the ontology were based on domain expert knowledge, literature review, standard vocabularies and upper ontologies (DUL, SSN/SOSA, AGROVOC and GEMET). Classes and class hierarchy, object and data properties were carefully defined to capture the concepts and relationships in the domain. Constraints on properties were specified and 74 instances were created based on a Kaggle beehive metrics dataset. 8 competence questions were predefined to determine the scope of knowledge, and later, 6 other related competence questions were defined and successfully executed based on internal hive temperature and hive weight by running SPARQL queries on the Apache Jena-Fuseki server. Domain expert validation and structural evaluation framework were applied and logical consistency was checked by HermiT reasoner in Prot & eacute;g & eacute;, using completeness, relevance, accuracy and clarity as evaluation criteria.
