{'fraction_non_alphanumeric': 0.011152416356877323, 'fraction_numerical': 0.006195786864931847, 'mean_word_length': 4.9411764705882355, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Text2MARK: A text mining tool in the aid of knowledge representation

Representing knowledge in the beginning of the 21st century has become a challenge due to the large volume of information to which we are submitted. Associate intelligent techniques have become a relevant method in this context, however studies are still lacking with the aim of making the process simpler for the user. Considering the current state of the art of NLP text labelers, this proposal is based on texting efficiently and without complexity through the use of free texts. We evaluated this proposal using concept maps to represent knowledge of entity relationships and compared the maps with the approach of the conceptual model. This proposal was able to satisfy the representation with a correct percentage rate of over 90%.