TULIP: A Five-Star Table and List - From Machine-Readable to Machine-Understandable Systems
Currently, Linked Data is increasing at a rapid rate as the growth of the Web. Aside from new information that has been created exclusively as Semantic Web-ready, part of them comes from the transformation of existing structural data to be in the form of five-star open data. However, there are still...
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| Autores principales: | , |
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| Formato: | Capítulo de libro |
| Lenguaje: | inglés |
| Publicado: |
IntechOpen
2020
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| Materias: | |
| Acceso en línea: | https://www.intechopen.com/chapters/72003 |
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| Sumario: | Currently, Linked Data is increasing at a rapid rate as the growth of the Web. Aside from new information that has been created exclusively as Semantic Web-ready, part of them comes from the transformation of existing structural data to be in the form of five-star open data. However, there are still many legacy data in structured and semi-structured form, for example, tables and lists, which are the principal format for human-readable, waiting for transformation. In this chapter, we discuss attempts in the research area to transform table and list data to make them machine-readable in various formats. Furthermore, our research proposes a novel method for transforming tables and lists into RDF format while maintaining their essential configurations thoroughly. And, it is possible to recreate their original form back informatively. We introduce a system named TULIP which embodied this conversion method as a tool for the future development of the Semantic Web. Our method is more flexible compared to other works. The TULIP data model contains complete information of the source; hence it can be projected into different views. This tool can be used to create a tremendous amount of data for the machine to be used at a broader scale. |
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| ISBN: | 978-1-83962-671-5 |
| DOI: | 10.5772/intechopen.91406 |