Ontology Matching Through Absolute Orientation of Embedding Spaces

Costa, Guilherme; Stefani, Karolin; Kreplin, Katharina; Portisch, Jan; Paulheim, Heiko

doi:10.1007/978-3-031-11609-4_29

Cited by 4 publications

(1 citation statement)

References 4 publications

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“…The ontology alignment has been investigated in different research works like [1], [2], [8], [12], [17], [19] , [16]. While, there are a few works concerning formal ontology alignment in the state of the art.…”

Section: Related Workmentioning

confidence: 99%

Formal modeling of multi-viewpoint ontology alignment by mappings composition

Kolli

2023

Acta Universitatis Sapientiae, Informatica

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We propose a formal approach based on Bigraphical Reactive Systems (BRS) to provide a formal modeling of multi-viewpoint ontology alignment by composition systems’ structure using bigraphs their dynamic behaviors using bigraphical reaction rules. In the first phase of this approach, we address the modeling of the static structure the dynamic behavior of multi-viewpoint ontology alignment systems. We show how bigraphs enable the description of the di erent multi-view point ontology entities. Furthermore, we define a set of bigraphical reaction rules to model the dynamic nature of the alignment. We introduce composition strategies to describe multi-viewpoint ontology alignment systems’ behaviors. Then, we present a case study on which we illustrate the application of our proposed approach. Finally, we combine the logical reflection of Maude language the hierarchical structure of the BRS to provide an executable formal model for multi-viewpoint ontology alignment by composition systems.

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Section: Related Workmentioning

confidence: 99%

Formal modeling of multi-viewpoint ontology alignment by mappings composition

Kolli

2023

Acta Universitatis Sapientiae, Informatica

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Example Applications Beyond Node Classification

Paulheim

Ristoski

Portisch

2023

Synthesis Lectures on Data, Semantics, and Knowledge

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Matching ontologies with kernel principle component analysis and evolutionary algorithm

Xue,

Ye,

Nian

2024

Demonstratio Mathematica

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Ontology serves as a structured knowledge representation that models domain-specific concepts, properties, and relationships. Ontology matching (OM) aims to identify similar entities across distinct ontologies, which is essential for enabling communication between them. At the heart of OM lies the similarity feature (SF), which measures the likeness of entities from different perspectives. Due to the intricate nature of entity diversity, no single SF can be universally effective in heterogeneous scenarios, which underscores the urgency to construct an SF with high discriminative power. However, the intricate interactions among SFs make the selection and combination of SFs an open challenge. To address this issue, this work proposes a novel kernel principle component analysis and evolutionary algorithm (EA) to automatically construct SF for OM. First, a two-stage framework is designed to optimize SF selection and combination, ensuring holistic SF construction. Second, a cosine similarity-driven kPCA is presented to capture intricate SF relationships, offering precise SF selection. Finally, to bolster the practical application of EA in the SF combination, a novel evaluation metric is developed to automatically guide the algorithm toward more reliable ontology alignments. In the experiment, our method is compared with the state-of-the-art OM methods in the Benchmark and Conference datasets provided by the ontology alignment evaluation initiative. The experimental results show its effectiveness in producing high-quality ontology alignments across various matching tasks, significantly outperforming the state-of-the-art matching methods.

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Ontology Matching Through Absolute Orientation of Embedding Spaces

Cited by 4 publications

References 4 publications

Formal modeling of multi-viewpoint ontology alignment by mappings composition

Formal modeling of multi-viewpoint ontology alignment by mappings composition

Example Applications Beyond Node Classification

Matching ontologies with kernel principle component analysis and evolutionary algorithm

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