Description logic–based knowledge merging for concrete- and fuzzy-domain ontologies

Kumar, Somesh; Harding, Jennifer

doi:10.1177/0954405414564404

Cited by 8 publications

(5 citation statements)

References 30 publications

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“…The target ontology is the priority (preferred) ontology, whereas the source ontologies have a lower priority. The target ontology is also called the seed ontology [42], the backbone ontology [42] or the knowledge base [8,42]. The latter should be completely preserved during the integration process because it may already be in use by various applications or services.…”

Section: Asymmetric Mergementioning

confidence: 99%

“…Collaborative companies may not only share physical assets, but may also share knowledge that needs to be integrated; 3. Companies may need to update their current ontology by adding new knowledge [8] because of new business processes requirements; 4. Applications that rely on domain ontologies may need to use ontologies covering different perspectives on one domain; 5.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Ontology Integration: Approaches and Challenging Issues

2021

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Eliciting key definitions and notions related to the ontology integration area. • Reporting and analyzing integration principles, consequences and repair techniques. • Briefly reviewing the most relevant research works on the ontology integration area. • Performing a holistic ontology integration to highlight open issues from the survey. • Improving the ontology matching will considerably improve the ontology integration.

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Section: Asymmetric Mergementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ontology Integration: Approaches and Challenging Issues

2021

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“…Therefore, relationships between any two elements can be expressed by relationships between ontologies. 28,29…”

Section: Function-expression Modelmentioning

confidence: 99%

Conceptual design method driven by product genes

Ren

Yan

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part B:

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Genetics-based design is an effective approach to develop novel products for conceptual design. It could reduce innovation blindness by providing logically structured procedure. However, the major challenge of genetics-based engineering method is that how to identify what information is genetic information and how to use it in a conceptual design process. To solve this problem, this article proposes a conceptual design method driven by product genes. First, a functional expansion model is established based on analyzing the conceptual design process. Second, to respectively compare the functions and structure schemes in the model to biological traits and proteins, a product gene definition composed of behaviors and attributes is put forward. Then, a modeling and coding method of product genes is given analogous to that of biological genes. Third, operation technologies of product genes are analyzed, including breakdown, crossover, recombination, transcription, and translation. Based on this, a conceptual design method driven by product genes is advanced. Finally, an example shows that this method is able to extract key information of products and gives a method of how to use the information in a conceptual design process. Moreover, structure schemes obtained through this method are of high feasibility and have more possibilities of innovation.

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“…Therefore, most studies undertook to measure ontology similarity by measuring concept similarity, namely, semantic similarity and its computation methods [11]. There are such traditional methods as words similarity [12], context similarity [13,14] and structure similarity [15]. Other scholars have proposed a series of upgraded methods to compute the similarity of concepts, such as the synthetic method [16], concept grid [17] and their deformation methods [18].…”

Section: Related Workmentioning

confidence: 99%

Low-cost similarity calculation on ontology fusion in knowledge bases

Wen

Hui³

et al. 2019

Journal of Information Science

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Ontology fusion in knowledge bases has become less easy, due to the massive capacity involved in the process of semantic similarity calculation. Many similarity calculation methods have been developed, although they are hardly united. This article contributes a low-cost similarity calculation method for ontology fusion, based on the inspiration of binary metrics, with the aim of reducing the size of similarity calculations both spatially and logically. By introducing the definitions of a heterogeneous ontology, entities of ontologies and rules of ontology fusion on the basis of concept fusion and relationship fusion, we put forward the algorithm of main traverse procedure and calculated to be the least cost in time and space in comparison with traditional methods. We adopted three experiments to testify the usability of our approach from the perspective of actual library resources, small datasets and large datasets. In Experiment 1, the bibliographic data from East China Normal University Library were used to show the feasibility and capability of our proposal and present the process of the algorithm. In both Experiments 2 and 3, our approach had at least 88% confidence in detecting accurate merging mappings and also decreased time cost. The test demonstrated a good fusion result. The problem of lower recalls caused by error analysis results from the conflict between the complex structures in ontologies and the recursive functions, which will be improved in the future.

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Description logic–based knowledge merging for concrete- and fuzzy-domain ontologies

Cited by 8 publications

References 30 publications

Ontology Integration: Approaches and Challenging Issues

Ontology Integration: Approaches and Challenging Issues

Conceptual design method driven by product genes

Low-cost similarity calculation on ontology fusion in knowledge bases

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