**6. Conclusion**

Our proposal is different from existing research, which mainly efforts on transforming data from tables and lists into facts in various formats. TULIP focuses on extracting data from tables and lists into the dataset in the form of five-star Linked Data. Also, the RDF triples result can be used to recreate tables and lists in the same format as the source data because the designed schema focuses on the ability to preserve the structure of the original table and list. Another essential feature is that the acquired RDF triples can also be embedded in a package file such as XML or HTML with RDFa to be used to create tables and lists on a Webpage as an integrated dataset.

**Author details**

Thailand

**35**

Julthep Nandakwang\* and Prabhas Chongstitvatana

provided the original work is properly cited.

\*Address all correspondence to: julthep@nandakwang.com

Department of Computer Engineering, Chulalongkorn University, Bangkok,

*TULIP: A Five-Star Table and List - From Machine-Readable to Machine-Understandable…*

*DOI: http://dx.doi.org/10.5772/intechopen.91406*

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

TULIP can also be applied to many applications since it is designed to be very flexible. Implementers can choose to use any of its schema models, depending on their usage. It also supports many types of data and is extensible. Actually, TULIP also supports the Document Object Model (DOM) which can transform paragraphs or text blocks into the same structure. It can be used to transforms Wikipedia articles into TULIP format as a five-star open dataset so that the Semantic Web application can consume Linked Data more conveniently. All of this is to create a data structure that is not just machine-readable but will be machineunderstandable.

*TULIP: A Five-Star Table and List - From Machine-Readable to Machine-Understandable… DOI: http://dx.doi.org/10.5772/intechopen.91406*
