{'fraction_non_alphanumeric': 0.01993704092339979, 'fraction_numerical': 0.002098635886673662, 'mean_word_length': 5.402684563758389, '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}}
ToxEdit Improving Synthetic XML Data Generation through Document Analysis

Database schemas are very helpful in many settings, such as query optimization, data integration, synthetic data generation, etc. Particularly on the Web, where there is an abundance of data, schemas are invaluable for allowing the query and integration of disparate sources. Moreover, several Web data sources come with no schema at all. In this work, we explore efficient extraction of probabilistic XML schemas and propose a generic, customizable implementation of schema extraction and modification that is usable across application areas. This would make it easier to build application-specific tools that include automatic schema creation. Such a generic implementation can also be a key component within a more comprehensive model management approach, where the mapping returned by a such tool may be used as input to operations to merge schemas and compose mappings[10].