Graph matching is a cornerstone of ontology alignment, enabling semantic interoperability across heterogeneous knowledge sources. Traditional approaches, which rely on terminological, structural, or contextual similarities, often fail to capture the subtle and complex semantic relationships inherent in ontologies. In this paper, we propose a novel graph matching framework leveraging multidimensional embeddings to address these limitations. By projecting ontology entities into a high-dimensional vector space, our method encodes both structural and semantic interdependencies, enabling more accurate and efficient identification of correspondences. We introduce Graph Matching via Multidimensional Embeddings (GMME), a novel framework for ontology alignment. GMME leverages a multi-stage pipeline comprising ontology graph construction, embedding generation, similarity computation, and alignment refinement. Our framework employs adaptive thresholding and advanced similarity metrics, such as cosine similarity and scoring functions, to enhance alignment precision. The experimental investigation analyses the performance of GMME on the benchmark track supplied by the Ontology Alignment Evaluation Initiative (OAEI) and evaluates the impact of embedding dimensions on alignment accuracy. Experimental findings demonstrate the effectiveness of our GMME framework, particularly in scenarios requiring the resolution of complex, many-to-many correspondences. This work contributes to the broader domain of knowledge graph integration and provides a foundation for future research on scalable, domain-agnostic alignment techniques.

Medical ontologies have become indispensable in modern medicine, enabling the structuring of clinical knowledge, the standardization of terminology, and seamless semantic interoperability. Their extensive applications in electronic health records, clinical decision support systems, and research underscore their critical role in managing complex healthcare data. However, heterogeneity among independently developed ontologies introduces challenges that hinder efficient knowledge integration and interoperability. In this paper, we introduce a novel framework, Hierarchical Embedding for Ontology Matching (HEOM), based on hierarchical embedding techniques. The method captures structural, semantic, and contextual relationships, ensuring effective alignment of complex ontologies. By preserving hierarchical dependencies, HEOM improves the robustness and scalability of semantic interoperability in health-care systems. Our experiments, conducted using benchmark datasets from the Ontology Alignment Evaluation Initiative (OAEI), demonstrate significant improvements in precision, recall, and F-measure, highlighting the framework’s potential to advance intelligent and interoperable healthcare systems.

Vehicle Re-Identification involves identifying and matching a target vehicle with images captured from different views in a multi-camera network. This topic holds significant importance in various applications including intelligent transportation systems, video surveillance and smart city. However, Vehicle Re-Identification faces significant challenges in dynamic environments due to viewpoint variations, inter-vehicle appearance similarity, intra-class variability, illumination variation, occlusion and background clutter. To address these limitations, we propose HA-VReID, An Effective Hard Attention Model with Deep Learning for Vehicle Re-Identification that combines Hard attention mechanism for background removal and vehicle shape focus with EfficientNet-powered feature extraction for robust vehicle representation. Extensive experiments on the VeRi-776 and VRAI benchmarks demonstrate that our approach outperforms state-of-the-art methods in vehicle Re-Identification tasks.