{'fraction_non_alphanumeric': 0.02063273727647868, 'fraction_numerical': 0.006189821182943604, 'mean_word_length': 4.866935483870968, '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}}
Predicting Semantic Categories in Text Based on Knowledge Graph Combined with Machine Learning Techniques

ABSTRACT The Quran and the Sunnah are the two principal elements of the Islamic religion, and the hadith is an interpreter of the Quran. Hadith is everything that the Messenger Muhammad said, whether it was a word, an action, or a good adjective of the Prophet. Given the status of the hadith of Muslims everywhere in the world, digging into it is the m ain perspective to evoke the guiding principles and institutions that Muslims must follow. The mining of the hadith has received much attention in recent times, but so far, the work has not been fully implemented. This study focuses on predicting the semantic categories of the unclassified hadith text based on its text. The model can distinguish between several categories to predict the optimal one such as ablution, fasting, Hajj, and Zakat. To achieve this goal, a Knowledge-Graphic (KG) prediction model was developed to improve machine learning classifiers from the standpoint of two unique traits. 1) Define pivotal terms that have high values. II) Taking into account all the paths of those pivotal terms through their convergence with the categories in the Knowledge-Graphic of all the paths that link them. We rely on six books with more than 30,000 hadith and 120 classifications. Empirically, we found optimistic results in combining the KG model and machine learning classifiers.