1. A computer-implemented system for creating a hierarchical semantic map of a document and extracted information, comprising: a processor and a non-transitory computer readable medium storing processor executable instructions configured to cause the processor to: apportion, with the processor, the document into major sections by accessing the document, recognizing a hierarchical structure of the document, and dividing the document into the major sections by using a data profiler and a machine learning module wherein the data profiler and the machine learning module are pre-determined and saved in a database; classify, with the processor, the major sections of the document by using a classification with multiple levels from the machine learning module, and map the major sections to key elements in one of the multiple levels; search, with the processor, one major section that is mapped to one key element, and identify sub-sections within the one major section to achieve a maximum confidence score based on the machine learning module, wherein the maximum confidence score indicates that the sub-sections associate with the key element, and the sub-sections further contain granular level pieces of information comprising sub-granular clause types forming the key element according to the machine learning module; extract the granular level pieces of information comprising the sub-granular clause types from the identified sub-sections by using sequence modelers and linguistic characteristics provided by the data profiler, generate the hierarchical semantic map of the document by using the extracted information according to the hierarchical structure, and store the extracted information and the hierarchical semantic map associations in the database; and present in a user interface in a user display device drop down selections of the key elements of the document, and in response to a selection of one of the key elements, display the extracted information associated with the selected key element.