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.

In this paper, we introduce a novel method for embedding weighted graphs, designed to capture the significance of relationships in real-world weighted graph data. Unlike existing models that treat all relationships equally, our approach incorporates edge weights directly into the embedding process, enhancing the representation of highly weighted connections. By introducing modifications to both the scoring and loss functions, our method emphasizes the importance of weighted relationships during training. Theoretical analysis shows that our model maintains efficiency and scalability while significantly improving the capacity to represent weighted graph structures. Experimental results demonstrate that our approach consistently outperforms baseline methods, including TransE, in tasks involving weighted relationships, showcasing its robustness and applicability.

Knowledge graph embedding has emerged as a fundamental technique to represent entities and relationships in knowledge graphs within low-dimensional vector spaces. Among these methods, translation-based approaches stand out by treating relations as translations from head entities to tail entities, achieving state-of-the-art results. However, the training process of these methods can be prohibitively time-consuming, especially for large knowledge graphs, posing significant challenges in practical applications. As knowledge graphs grow in size and complexity, surpassing the capacities of existing systems, there is an urgent need for scalable solutions in knowledge representation learning. These graphs, comprising millions of nodes and billions of edges, serve as powerful data structures for representing and understanding complex networks of knowledge. Translation-based models, particularly TransE, have been instrumental in encoding structured information about entities and their relationships in low-dimensional embedding spaces. However, the current implementation of TransE is constrained to single-node machines, limiting its scalability and applicability. To address these limitations, this paper proposes leveraging parallel computing technique, such as parallel TransE with graph partitioning, to enhance scalability and efficiency in knowledge graph embedding.

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.

Topic Modeling (TM) can act as a bridge linking unstructured text data to a structured knowledge representation in a Knowledge Graph (KG). The Latent Dirichlet Allocation (LDA) is a commonly used distributional term clustering technique in this regard. However, existing distributional term clustering approaches have not utilized LDA as a bottom-up training strategy with prior knowledge to cluster semantically related terms as concepts across different domains for building and enhancing a GSCKG. We propose to employ a Twice-Trained Agglomerative hierarchical framework using LDA over Generic Semantic Core KG (T2AggLDA-GSCKG), outlined in five steps. We aim to align term topics with the predefined Core Concepts (CCs) of SCKGs, thereby designating modules of a GSCKG that facilitate both its building and enrichment. Our goal is to adapt the LDA term clustering process by utilizing a topic seed-based LDA model to consider these CCs. During the 1stLDA training, we endeavor to identify hypernym and related relations among noun phrase patterns to construct SCKG, and during the 2ndtraining, we aim to enhance it. To achieve this, we will boost the 2ndtraining LDA input data by benefiting from CCs’ two prior knowledge techniques for topic-seed terms incorporation namely seed key knowledge injection and entity masking. The evaluation results show that our proposal has an overwhelming term clustering performance over CCs. It outperforms other unsupervised and semi-supervised distributional baselines on two datasets related to fish hunting and ontology domains, with nearly 13 times higher precision compared to that of normal LDA training.

Heterogeneous graphs (HG) are an effective way of abstracting complex systems, including social, biological, and economic systems. However, modeling these graphs is challenging due to their high dimensionality, sparsity, and heterogeneity. Traditional approaches designed for homogeneous graphs struggle to handle the diverse types of entities and relationships present in HG, leading to a loss of information and potentially inaccurate embeddings. To address these challenges, we introduce a novel method, Trans-Trip: Translation-based embedding with Triplets for Heterogeneous Graphs, that leverages the power of triplets of (entity, relation, entity) to accurately represent the various types of relationships among nodes and links. Trans-trip effectively captures the rich semantics embedded in HGs, overcoming the challenges of global coherence and entity projection. By leveraging the flexibility and interpretability of triplets, our method can handle the multi-types nodes/links present in HG and can capture complex higher-order structures. We demonstrate the effectiveness of our proposed method on several benchmark datasets, showing that it outperforms existing embedding methods. Trans-trip provides a more accurate and interpretable representation of HG, which can be used across various fields, such as biology, social networks, and e-commerce.

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.