Review implementation of linguistic approach in schema matching

Martono, Galih Hendro; Sn, Azhari

doi:10.26555/ijain.v3i1.75

Cited by 4 publications

(2 citation statements)

References 25 publications

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“…Penulis [11] menyatakan bahwa teknik Linguistik yang dapat menentukan kesamaan antar entitas dapat digunakan sebagai metode schema matching. Penulis [12] juga menyatakan hal yang sama.…”

Section: Beberapaunclassified

Metode Schema Matching berbasis Linguistic dan Constraint untuk Integrasi Database di Sekolah

Raharjo

Utami

Alsyaibani

2022

JEPIN

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Integrasi data kini telah menjadi kebutuhan bagi setiap organisasi. Organisasi yang tidak mengintegrasikan data antara satu divisi dengan divisi lainnya akan menimbulkan kemungkinan terjadinya redundansi dan data yang tidak konsisten. Proses integrasi data umumnya terkendala oleh penggunaan istilah dan penulisan nama tabel serta atribut yang berbeda. Dalam penelitian ini, metode Linguistik dikombinasikan dengan metode Constraint digunakan untuk menemukan kesamaan antara atribut database yang berbeda. Metode Bigram digunakan sebagai metode linguistik. Atribut yang diusulkan untuk dihapus oleh Bigram ditinjau kembali dari aspek Constraint. Dengan menggunakan metode ini, 8 tabel dan 60 atribut dapat direduksi dari tujuh database. Hasil penelitian menunjukkan bahwa metode ini mempunyai akurasi di atas 99% pada semua skenario. Di sisi lain, Precision terendah terdapat saat membandingkan atribut antara database Administrasi_siswa dan Kesiswaan, yaitu hanya mencapat 70%. Meskipun masih ada beberapa kesalahan yang dilakukan oleh Bigram sebagai metode Linguistik, kesalahan tersebut dapat ditutupi dengan menggabungkan metode tersebut dengan metode berbasis Constraint. Pengujian validitas hasil integrasi dilakukan dengan query menggunakan sintaks SQL langsung ke database dan menghasilkan hasil query yang benar.

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“…Penulis [11] menyatakan bahwa teknik Linguistik yang dapat menentukan kesamaan antar entitas dapat digunakan sebagai metode schema matching. Penulis [12] juga menyatakan hal yang sama.…”

Section: Beberapaunclassified

Metode Schema Matching berbasis Linguistic dan Constraint untuk Integrasi Database di Sekolah

Raharjo

Utami

Alsyaibani

2022

JEPIN

View full text Add to dashboard Cite

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“…Whereas the linguistic-based method matching scheme is one of the matching schema methods related to the similarity or similarity of the element names in a scheme both syntactically and semantically [17]. The linguistic method is a scheme matching method that is carried out by looking at the similarity of the naming of elements in a database [18]. The linguistic method used is N-Gram.…”

Section: Introductionmentioning

confidence: 99%

Optimization of data integration using schema matching of linguistic-based and constraint-based in the university database

Hammad

Nurcahyo²,

Amrullah

et al. 2021

MATRIX

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University requires the integration of data from one system with other systems as needed. This is because there are still many processes to input the same data but with different information systems. The application of data integration generally has several obstacles, one of which is due to the diversity of databases used by each information system. Schema matching is one method that can be used to overcome data integration problems caused by database diversity. The schema matching method used in this research is linguistic and constraint. The results of the matching scheme are used as material for optimizing data integration at the database level. The optimization process shows a change in the number of tables and attributes in the database that is a decrease in the number of tables by 13 tables and 492 attributes. The changes were caused by some tables and attributes were omitted and normalized. This research shows that after optimization, data integration becomes better because the data was connected and used by other systems has increased by 46.67% from the previous amount. This causes the same data entry on different systems can be reduced and also data inconsistencies caused by duplication of data on different systems can be minimized.

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Unsupervised Semantic Mapping for Healthcare Data Storage Schema

Satti

Hussain

et al. 2021

IEEE Access

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Data, information, and knowledge processing systems, in the domain of healthcare are currently plagued by the heterogeneity at various levels. Current solutions, have focused on developing a standard based, manual intervention mechanism, which requires a large amount of human resource and necessitate realignment of existing systems. State-of-the-art methodologies in the field of natural language processing and machine learning, can help to partially automate this process, reducing the resource requirements and providing a relatively good multi-class based classification algorithm. We present a novel methodology for bridging the gap between various healthcare data management solutions by leveraging the strength of transformer based machine learning models, to create mappings between the data elements. Additionally, the annotated data, collected against five medical schemas and labeled by 4 annotators is made available for helping future researchers. Our results indicate, that for biased, dependent multi-class text classification, RoBERTa shows the best performance, achieving a Cohen's kappa score of 0.47 and Matthews Correlation Coefficient (MCC) score of 0.48, with human annotated data.

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Review implementation of linguistic approach in schema matching

Cited by 4 publications

References 25 publications

Metode Schema Matching berbasis Linguistic dan Constraint untuk Integrasi Database di Sekolah

Metode Schema Matching berbasis Linguistic dan Constraint untuk Integrasi Database di Sekolah

Optimization of data integration using schema matching of linguistic-based and constraint-based in the university database

Unsupervised Semantic Mapping for Healthcare Data Storage Schema

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