{'fraction_non_alphanumeric': 0.027816411682892908, 'fraction_numerical': 0.005563282336578581, 'mean_word_length': 5.101694915254237, '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}}
HIMA: A Holistic Data Instance Matching Approach

Considering the consistency of instance level, we come up with a Holistic Data Instance Matching Approach (HIMA). Firstly, we measure the similarity of instances with the algorithm of string distances. HIMA makes use of the clustering algorithm, which it can handle, a large scale of data source holistically. In addition, we use the keyword extracting method, which is based on the maximum entropy model, to get rid of the useless information. The experimental results show that the keyword extracting algorithm can get 70% precision, and the condition probabilistic based algorithm is more precise than the token-based algorithm. HIMA method can achieve 83% accuracy.