Ontology Matching using Multiple Similarity Measures

Nguyen, Thi Thuy Anh; Conrad, Stefan

doi:10.5220/0005615606030611

Cited by 10 publications

(5 citation statements)

References 19 publications

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“…Ontology matchers are used to calculate the extent to which two concepts are similar Pirro and Talia (2008), which is a real number between 0 and 1. We propose a hybrid ontology matcher that combines three categories of ontology matchers Nguyen and Conrad (2015); Xue (2023) to construct a profile-based matcher Xue, Lu, and Chen (2019), which comprehensively evaluates the similarity between ontologies.…”

Section: Ontology Matchermentioning

confidence: 99%

Heterogeneous Ontology Alignment Extraction For E-Business Through Compact Co-Evolutionary Niching Genetic Algorithm

Lian¹,

Li²

2023

Preprint

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Ontologies provide a standardized approach to knowledge representation that can be shared across various domains. By extracting heterogeneous ontology alignment, E-businesses can efficiently exchange information and enhance communication, decision-making, and reduce data integration costs. In this study, we investigate the heterogeneous ontology alignment extraction problem for E-business, which aims to determine an optimal concept pair set with the highest f-measure value. Given the alignment extraction’s inherent complexity, we use a Genetic Algorithm (GA) to address it. In particular, we first model the HOAEP as a multi-modal problem with sparse solutions and then propose a Compact Co-Evolutionary Niching Genetic Algorithm (CCNGA) to address it. CCNGA first employs probability distribution estimation to simplify population representation, and then uses three evolutionary strategies to simultaneously search for the global optimum. The experimental testing cases include OAEI’s Conference track and three real E-business ontologies, and T-test results demonstrate that CCNGA significantly outperforms other state-of-the-art ontology alignment extraction techniques.

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Section: Ontology Matchermentioning

confidence: 99%

Heterogeneous Ontology Alignment Extraction For E-Business Through Compact Co-Evolutionary Niching Genetic Algorithm

Lian¹,

Li²

2023

Preprint

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“…To completely explore semantics implicated in ontology (see [37], [38] for definitions of formalized semantics) and directly manipulate the reasoning process, proper reasoning algorithms should be selected to analyze semantic information during the ontology matching process.…”

Section: Future Workmentioning

confidence: 99%

“…There are two types of DL reasoning algorithms [39], [44]: structure subsumption reasoning algorithms and tableau algorithms. The former algorithm is typically used for non-standard reasoning problems [37], while the latter algorithm is typically used by reasoners (e.g., Pellet and Racer). When confronting constructors or axioms in ontology, tableau algorithms add individuals according to certain rules and check whether the ontologies are consistent.…”

Section: Future Workmentioning

confidence: 99%

“…However, these schemes fail to define formalized semantics of the information analysis results, as well as the relation between the final correspondence and ontology semantics. Therefore, it is not properly interpreted in the aggregation methods, including the interpretation of the domain I  and the function () I  (refer to [37], [38] for a definition of the interpretation).…”

Section: B Challengesmentioning

confidence: 99%

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Ontology Matching: State of the Art, Future Challenges, and Thinking Based on Utilized Information

et al. 2021

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Information used in existing ontology matching solutions are usually grouped into four categories: lexical information, structural information, semantic information, and external information, respectively. By summarizing and analyzing the approaches for utilizing the same kind of information, this paper finds that lexical information is mainly analyzed based on text and dictionary similarity. Similarly, structural information and semantic information are mainly analyzed based on graph structure and reasoner, respectively. The approaches for aggregating information analysis results are discussed. Challenges in the analysis of various types of information for existing ontology matching solutions are also described, and insights into directions for future research are provided.

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“…Finally, semantic methods are based on the external ontologies. In Nguyen and Conrad (2015) and Essayeh and Abed (2015), the authors develop hybrid methods (combining structural and semantic).…”

Section: Ontology Alignmentmentioning

confidence: 99%

A Demand-Response System for Sustainable Manufacturing Using Linked Data and Machine Learning

Wicaksono

Boroukhian

Bashyal

2021

Dynamics in Logistics

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The spread of demand-response (DR) programs in Europe is a slow but steady process to optimize the use of renewable energy in different sectors including manufacturing. A demand-response program promotes changes of electricity consumption patterns at the end consumer side to match the availability of renewable energy sources through price changes or incentives. This research develops a system that aims to engage manufacturing power consumers through price- and incentive-based DR programs. The system works on data from heterogeneous systems at both supply and demand sides, which are linked through a semantic middleware, instead of centralized data integration. An ontology is used as the integration information model of the semantic middleware. This chapter explains the concept of constructing the ontology by utilizing relational database to ontology mapping techniques, reusing existing ontologies such as OpenADR, SSN, SAREF, etc., and applying ontology alignment methods. Machine learning approaches are developed to forecast both the power generated from renewable energy sources and the power demanded by manufacturing consumers based on their processes. The forecasts are the groundworks to calculate the dynamic electricity price introduced for the DR program. This chapter presents different neural network architectures and compares the experiment results. We compare the results of Deep Neural Network (DNN), Long Short-Term Memory Network (LSTM), Convolutional Neural Network (CNN), and Hybrid architectures. This chapter focuses on the initial phase of the research where we focus on the ontology development method and machine learning experiments using power generation datasets.

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Ontology Matching using Multiple Similarity Measures

Cited by 10 publications

References 19 publications

Heterogeneous Ontology Alignment Extraction For E-Business Through Compact Co-Evolutionary Niching Genetic Algorithm

Heterogeneous Ontology Alignment Extraction For E-Business Through Compact Co-Evolutionary Niching Genetic Algorithm

Ontology Matching: State of the Art, Future Challenges, and Thinking Based on Utilized Information

A Demand-Response System for Sustainable Manufacturing Using Linked Data and Machine Learning

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