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.

Ontology alignment is a method that automatically establishes the semantic similarities between the concepts of two ontologies. The ontology alignment adjustments aim to identify concepts that are semantically equivalent across multiple ontologies. However, simple ontology alignments are a domain largely studied. It attaches one entity from the source ontology to a corresponding entity in the target ontology. However, one limitation of these alignments is their lack of interpretability, which can be mitigated by employing complex matching. In attempt to deal with it, we introduce a complex ontology alignment model based on Lexical Indexation. We have carried out a thorough evaluation using datasets from the Ontology Alignment Evaluation Initiative (OAEI). The findings are promising, indicating that our proposed scheme is practical.

Complex matching ontology involves measuring the distance between ontological elements. The objective of complex matching ontology is to locate semantically equivalent concepts in two ontologies. The main focus of current complex matching methods is on capturing features related to terminological, structural, and contextual semantics in ontologies. The features of based techniques are intensive but also neglect the hidden semantic relations in ontologies. In this study, we introduce a complex matching ontology model based on hyperbolic geometry embedding. We examine the Hyperbolic geometry embedding for measuring similarity between ontologies, and discovering complex matchings between elements. Our experiments with the Ontology Alignment Evaluation Initiative (OAEI) achieved competitive results compared to the leading systems.

The semantic heterogeneity issue in the information integration can be solved by using ontology alignment. The goal of the ontology alignment technique is to locate concepts that are semantically identical in two ontologies. But, one of these alignments’ drawbacks is their lack of expressiveness, which can be accounted by using complex alignments. To deal with it, an effective strategy is to model ontologies in vector space and compute their similarity scores to determine the correlation levels. In this paper, a semantic embedding-based ontology matching technique for OWL 2 ontologies is employed to calculate entities’ resemblance in order to increase the alignment accuracy. This technique is reinforced by a stable marriage-based alignment extraction algorithm to establish high-quality matching. The experimental investigation analyses the performance to the benchmark track supplied by the Ontology Alignment Evaluation Initiative (OAEI). Experimental findings demonstrated the effectiveness of our matching method.

Clearly, ontology components are distinguished depending on features of inexactitudes and uncertainties. Such shortcomings are mostly the outcome an indistinct inaccurate semantic lingual representation, supplied by professionals. So as to tackle this lack of exactness issue, the notion of ”fuzziness” has to be considered. In this respect, fuzzy ontologies have been shown to be useful tools to represent specific knowledge (crisp and fuzzy) and reasoning over it. Thus, an advanced fuzzy ontology called the COVID-19 Fuzzy Ontology (CFO) is exhibited in this work. The latter licenses a semantically meaningful representation of fuzzy crusty medical data particulars related to the diagnosis of COVID-19. The CFO also takes into account the imprecise aspects associated to the induced knowledge of this disease. The CFO is grounded from a domain ontology about COVID-19. The evaluation of the CFO ontology shows that it is accurate, consistent, and that it covers COVID-19 terminologies.