{'fraction_non_alphanumeric': 0.02131782945736434, 'fraction_numerical': 0.0, 'mean_word_length': 5.416149068322981, '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}}
A Link Prediction Model Based on Improved TuckER Decomposition and Neural Tensor Network

Knowledge graphs usually refer to a knowledge base that stores a large number of triples. It uses triples (head entity, relationship, tail entity) to represent the relationship between entities, which can provide practical and valuable information for disciplinary research. However, they are often incomplete because their construction may be based on only a fraction of all facts, and the implicit relationships among a large number of entities are not sufficiently mined. To solve this problem, we can perform link prediction on the missing parts. Any existing knowledge graph needs to continuously improve the knowledge itself through completion, and even infer new knowledge. This paper will combine the improved Tucker decomposition algorithm and neural tensor network (NTN) to propose a new link prediction algorithm called Improved TuckER-NTN (Im Tu-NTN), which can effectively improve the accuracy of knowledge graph link prediction.