This paper concerns an Information Extraction process for building a dynamic Legislation Network from legal documents. Unlike supervised learning approaches which require additional calculations, the idea here is to apply Information Extraction methodologies by identifying distinct expressions in legal text and extract quality network information. The study highlights the importance of data accuracy in network analysis and improves approximate string matching techniques for producing reliable network data-sets with more than 98 percent precision and recall. The values, applications, and the complexity of the created dynamic Legislation Network are also discussed and challenged.