**Abstract**

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 Webready, 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.

**Keywords:** data labeling, knowledge discovery, knowledge representation, Linked Data, open data, semantic annotation, Semantic Web
