The increasing need for culturally aware and semantically interoperable healthcare systems presents a major challenge for leveraging artificial intelligence in clinical decision support. Global standards such as SNOMED CT, ICD-10, and LOINC provide solid foundations for interoperability but lack the contextual richness required to represent region-specific medical practices and multilingual healthcare environments. This study presents and evaluates ShefaaOnto, a culturally enriched and standards-aligned healthcare ontology developed for Saudi Arabia that captures the country's distinctive medical, linguistic, and cultural characteristics. It addresses key national healthcare needs, including epidemic disease management (e.g., MERS-CoV), integration of traditional practices such as Hijama, and health logistics during large-scale gatherings like Hajj and Umrah. Although developed for Saudi Arabia, ShefaaOnto's modular architecture and explicit semantic mappings to international terminologies support adaptation to other culturally distinctive healthcare ecosystems. The ontology was validated through simulated clinical scenarios, expert evaluation, and SPARQL query benchmarking (300 queries across three representative use cases), together with a separate expert-grounded evaluation over 1,000 synthetically generated, clinically informed patient profiles. On the SPARQL benchmark, queries deterministically returned the answer sets defined by a frozen, expert-validated reference graph, confirming reproducible retrieval across the three use cases. On the expert-grounded evaluation, ShefaaOnto's inferred outputs matched Ministry of Health–aligned reference answers with a case-level exact-match accuracy of 95% (950/1,000 cases) and entity-level precision and recall of 93% and 96%, respectively (F1 = 94.5%). Compared with a MySQL relational baseline, ShefaaOnto demonstrated greater reasoning completeness for semantically complex queries, while sustaining only 9.8% latency growth under 10,000 concurrent queries, compared with 32.1% for MySQL. These findings support ShefaaOnto's semantic reliability as a culturally contextual healthcare knowledge representation framework and illustrate a transferable ontology-engineering approach for developing locally grounded yet globally interoperable healthcare knowledge systems.
We introduce a semantically grounded reporting model for web accessibility audits, published as an RDF vocabulary with SHACL validation profile, JSON-LD context, and SPARQL competency queries, enabling interoperable, queryable, provenance-complete audit knowledge graphs. The vocabulary comprises eight core classes (AuditRun, Finding, Criterion, Target, Evidence, Recommendation, ModelInvocation, PageState) with 40+ properties, building on PROV-O, Web Annotation, Dublin Core, and SKOS standards. Twelve declarative SHACL shapes enforce structural integrity, semantic consistency and provenance completeness. The model supports dual anchoring of evidence (DOM selectors and visual bounding box coordinates) to bridge the code-based and perceptual assessment paradigms. We instantiated the framework with a vision–language model (LLaVA-1.5) to demonstrate visual-first detection for WCAG criteria 1.1.1, 1.4.3, 2.4.2 and 2.4.4 on 270 WebSight samples. For criteria 1.1.1 and 1.4.3, the pipeline achieves 84% human-verified precision (Cohen’s κ=0.74, N=50 gold-standard annotations). A real-world pilot on 42 of 50 public websites shows flag rates of 38.1% and 31.0% for the same criteria, constituting evidence of deployment feasibility. Comparative evaluation against axe-core 4.9 reveals that approximately 52% of VLM flags are candidate findings not detected by the specific axe-core 4.9 configuration, including violations unreachable by DOM attribute analysis. Semantic artifacts enable cross-run regression analysis, evidence filtering, and reproducibility in accessibility workflows, positioning accessibility auditing as infrastructure compatible with semantic web ecosystems.
The correctness of knowledge graphs is crucial for their successful application. However, knowledge graphs often contain errors, making knowledge cleaning, i.e., the detection and correction of errors, essential. This survey presents a structured review of approaches for cleaning assertion errors in knowledge graphs based on the Resource Description Framework (RDF) data model. Its main contribution is a taxonomy that classifies cleaning approaches for the three most common assertion types: instance, property-value, and equality assertions. The taxonomy organizes approaches into 10 cleaning techniques and associates them with 6 background knowledge sources. Background knowledge may originate from the knowledge graph itself, including graph structure and contextual triples and values, or from external sources such as curated data, formal specifications, human expertise, and structured or unstructured sources. Together with its three facets and evaluation measures, the taxonomy provides a framework for understanding and comparing cleaning approaches based on knowledge graph characteristics, the available background knowledge, the targeted assertion type, and the cleaning process. The comparative analysis reveals that the diversity of knowledge graph cleaning approaches reflects the task’s inherent complexity, with different techniques leveraging complementary background knowledge sources. It also identifies open research challenges, including uneven support across assertion types and cleaning processes. Together, these insights provide practitioners with a systematic basis for selecting suitable cleaning approaches and researchers with a structured foundation for future work.
Knowledge graphs (KGs) have emerged as foundational infrastructures in modern natural language processing (NLP) and recommender systems. However, long-tailed relational distributions and structural sparsity pose significant challenges to few-shot knowledge graph completion (FKGC). Under extremely low-resource scenarios, conventional neural architectures are prone to overfitting, leading to suboptimal generalization capabilities. To address these issues, we propose RANS-KAN-DRIA, a dynamic meta-learning framework incorporating three core components: (1) a Relation-Aware Neighbor Selector (RANS), which adaptively integrates relation-relevant neighborhood context with support instances to strengthen relational reasoning; (2) a lightweight KAN-based Relational Meta-Learner, which operates on low-dimensional entity-pair representations from the few-shot support set and constructs nonlinear and topology-aware relational features through Kolmogorov–Arnold decomposition; and (3) a Diffusion-Regularized Inner-loop Adapter (DRIA), which enables rapid parameter adaptation via gradient updates and stabilizes model performance using diffusion-generated hard negatives. Experimental results on two benchmark datasets demonstrate that RANS-KAN-DRIA achieves strong performance in few-shot scenarios. The proposed framework contributes a task-specific integration of relation-aware neighborhood selection, lightweight KAN-based relational meta-learning, and diffusion-regularized inner-loop adaptation for robust FKGC under extreme sparsity.
