The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Kami mencadangkan kaedah yang dipertingkatkan untuk menterjemah Peta Topik kepada Skema RDF/RDF, untuk merealisasikan Web Semantik. Isu kritikal untuk Web Semantik adalah untuk menerangkan sumber maklumat Web dengan cekap dan tepat, iaitu, metadata Web. Dua standard yang mewakili, Peta Topik dan RDF telah digunakan untuk metadata Web. Penyeragaman dan pelaksanaan Web Semantik berasaskan RDF telah dilaksanakan secara aktif. Memandangkan Web Semantik mesti menerima dan memahami semua sumber maklumat Web yang diwakili dengan kaedah lain, terjemahan Peta Topik-ke-RDF telah menjadi isu. Walaupun banyak kaedah penterjemahan Peta Topik ke RDF telah direka, kaedah tersebut masih mempunyai beberapa masalah (cth kehilangan semantik, ungkapan kompleks, dsb.). Kaedah terjemahan kami menyediakan penyelesaian yang lebih baik untuk masalah ini. Kaedah ini menunjukkan kehilangan semantik yang lebih rendah daripada kaedah sebelumnya kerana mengekstrak kedua-dua semantik eksplisit dan semantik tersirat. Berbanding dengan kaedah sebelumnya, kaedah kami mengurangkan kerumitan pengekodan RDF yang terhasil. Selain itu, dari segi kebolehbalikan, kaedah yang dicadangkan menjana semula semua binaan Peta Topik dalam sumber asal apabila diterjemahkan secara terbalik.
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Salinan
Shinae SHIN, Dongwon JEONG, Doo-Kwon BAIK, "Novel Topic Maps to RDF/RDF Schema Translation Method" in IEICE TRANSACTIONS on Information,
vol. E91-D, no. 11, pp. 2626-2637, November 2008, doi: 10.1093/ietisy/e91-d.11.2626.
Abstract: We propose an enhanced method for translating Topic Maps to RDF/RDF Schema, to realize the Semantic Web. A critical issue for the Semantic Web is to efficiently and precisely describe Web information resources, i.e., Web metadata. Two representative standards, Topic Maps and RDF have been used for Web metadata. RDF-based standardization and implementation of the Semantic Web have been actively performed. Since the Semantic Web must accept and understand all Web information resources that are represented with the other methods, Topic Maps-to-RDF translation has become an issue. Even though many Topic Maps to RDF translation methods have been devised, they still have several problems (e.g. semantic loss, complex expression, etc.). Our translation method provides an improved solution to these problems. This method shows lower semantic loss than the previous methods due to extract both explicit semantics and implicit semantics. Compared to the previous methods, our method reduces the encoding complexity of resulting RDF. In addition, in terms of reversibility, the proposed method regenerates all Topic Maps constructs in an original source when is reverse translated.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e91-d.11.2626/_p
Salinan
@ARTICLE{e91-d_11_2626,
author={Shinae SHIN, Dongwon JEONG, Doo-Kwon BAIK, },
journal={IEICE TRANSACTIONS on Information},
title={Novel Topic Maps to RDF/RDF Schema Translation Method},
year={2008},
volume={E91-D},
number={11},
pages={2626-2637},
abstract={We propose an enhanced method for translating Topic Maps to RDF/RDF Schema, to realize the Semantic Web. A critical issue for the Semantic Web is to efficiently and precisely describe Web information resources, i.e., Web metadata. Two representative standards, Topic Maps and RDF have been used for Web metadata. RDF-based standardization and implementation of the Semantic Web have been actively performed. Since the Semantic Web must accept and understand all Web information resources that are represented with the other methods, Topic Maps-to-RDF translation has become an issue. Even though many Topic Maps to RDF translation methods have been devised, they still have several problems (e.g. semantic loss, complex expression, etc.). Our translation method provides an improved solution to these problems. This method shows lower semantic loss than the previous methods due to extract both explicit semantics and implicit semantics. Compared to the previous methods, our method reduces the encoding complexity of resulting RDF. In addition, in terms of reversibility, the proposed method regenerates all Topic Maps constructs in an original source when is reverse translated.},
keywords={},
doi={10.1093/ietisy/e91-d.11.2626},
ISSN={1745-1361},
month={November},}
Salinan
TY - JOUR
TI - Novel Topic Maps to RDF/RDF Schema Translation Method
T2 - IEICE TRANSACTIONS on Information
SP - 2626
EP - 2637
AU - Shinae SHIN
AU - Dongwon JEONG
AU - Doo-Kwon BAIK
PY - 2008
DO - 10.1093/ietisy/e91-d.11.2626
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E91-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2008
AB - We propose an enhanced method for translating Topic Maps to RDF/RDF Schema, to realize the Semantic Web. A critical issue for the Semantic Web is to efficiently and precisely describe Web information resources, i.e., Web metadata. Two representative standards, Topic Maps and RDF have been used for Web metadata. RDF-based standardization and implementation of the Semantic Web have been actively performed. Since the Semantic Web must accept and understand all Web information resources that are represented with the other methods, Topic Maps-to-RDF translation has become an issue. Even though many Topic Maps to RDF translation methods have been devised, they still have several problems (e.g. semantic loss, complex expression, etc.). Our translation method provides an improved solution to these problems. This method shows lower semantic loss than the previous methods due to extract both explicit semantics and implicit semantics. Compared to the previous methods, our method reduces the encoding complexity of resulting RDF. In addition, in terms of reversibility, the proposed method regenerates all Topic Maps constructs in an original source when is reverse translated.
ER -