XMap&amp;#x002B;&amp;#x002B;: A novel semantic approach for alignment of OWL-Full ontologies based on semantic relationship using WordNet

Khadir, Mohamed Tarek; Djeddai, Ala; Djeddi, Warith Eddine

doi:10.1109/isiict.2011.6149595

Cited by 4 publications

(2 citation statements)

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“…It can perform data query work at the semantic level, but its disadvantage is that the process is more complicated and the implementation is more difficult. OLA [16][17] is a data fusion system that maps from a grammatical point of view. The system mainly analyzes the constituent elements in the ontology, and uses the comprehensive value of the Hamming Distance and the tagging distance between elements to represent the semantic correlation between different elements.…”

Section: Related Workmentioning

confidence: 99%

Industrial data fusion method based on semantic data dictionary for digital twin

Liu

Zhang²

2023

Third International Symposium on Computer Engineering and Intelligent Communications (ISCEIC 2022)

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Industrial digital twin is the key support for the transformation of industry to intelligence. The realization of digital twin relies on the integration of whole-process data, which can provide real-time and intelligent decision-making optimization for production management. This paper proposes a digital twin data fusion method based on semantic data dictionary. Firstly, a modeling method of domain data dictionary is designed, and a semantic method is introduced to realize the construction of semantic data dictionary. Then, based on the semantic data dictionary, a semantic similarity calculation method based on multivariate distance weighting is proposed. Finally, the algorithm is tested and verified. The experimental results show that the method in this paper has a significant effect on automatic data fusion in the construction of industrial digital twins.

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Section: Related Workmentioning

confidence: 99%

Industrial data fusion method based on semantic data dictionary for digital twin

Liu

Zhang²

2023

Third International Symposium on Computer Engineering and Intelligent Communications (ISCEIC 2022)

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“…The vast majority of ontology matching research follows the feature engineering approach (Wang and Xu, 2008;Cruz et al, 2009;Khadir et al, 2011;Jiménez-Ruiz and Grau, 2011;Fahad et al, 2012;Ngo and Bellahsene, 2012;Gulić et al, 2016). Features are generated using a broad range of techniques (Anam et al, 2015;Harispe et al, 2015), ranging from the exploitation of terminological information, including structural similarities and logical constraints, such as datatype properties, cardinality constraints, etc.…”

Section: Selecting Features For Ontology Matchingmentioning

confidence: 99%

DeepAlignment: Unsupervised Ontology Matching with Refined Word Vectors

Kolyvakis¹,

Kalousis²,

Kiritsis³

2018

Proceedings of the 2018 Conference of the North American Chapter Of the Association for Computational Linguistics: Hu

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Ontologies compartmentalize types and relations in a target domain and provide the semantic backbone needed for a plethora of practical applications. Very often different ontologies are developed independently for the same domain. Such "parallel" ontologies raise the need for a process that will establish alignments between their entities in order to unify and extend the existing knowledge. In this work, we present a novel entity alignment method which we dub DeepAlignment. DeepAlignment refines pre-trained word vectors aiming at deriving ontological entity descriptions which are tailored to the ontology matching task. The absence of explicit information relevant to the ontology matching task during the refinement process makes DeepAlignment completely unsupervised. We empirically evaluate our method using standard ontology matching benchmarks. We present significant performance improvements over the current state-of-the-art, demonstrating the advantages that representation learning techniques bring to ontology matching.

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Accurate Identification of Ontology Alignments at Different Granularity Levels

Feng

Chen

et al. 2017

IEEE Access

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XMap++: A novel semantic approach for alignment of OWL-Full ontologies based on semantic relationship using WordNet

Cited by 4 publications

References 4 publications

Industrial data fusion method based on semantic data dictionary for digital twin

Industrial data fusion method based on semantic data dictionary for digital twin

DeepAlignment: Unsupervised Ontology Matching with Refined Word Vectors

Accurate Identification of Ontology Alignments at Different Granularity Levels

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