{'fraction_non_alphanumeric': 0.02356020942408377, 'fraction_numerical': 0.003926701570680628, 'mean_word_length': 4.97265625, '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}}
Good and bad modeling practices

The basic assumptions behind the Semantic Web—the AAA (Anyone can say Anything about Any topic), Open World, and Nonunique Naming assumptions—place very specific restrictions on the modeling language. The structure of RDF (Resource Description Framework) is in the form of statements with familiar grammatical constructs like subject, predicate, and object. The structure of OWL (Web Ontology Language) includes familiar concepts like class and property. But the meaning of a model is given by the inference rules of OWL, which incorporate the assumptions of the Semantic Web. All of these models are valid expressions in RDF/OWL, but they are erroneous in the sense that they do not accomplish what the modeler intended by creating them. In each case, the mismatch can be revealed through careful examination of the inferences that the model entails. In some cases (like the objectification error), the requirements themselves are inconsistent with the Semantic Web assumptions. In other cases (like the exclusivity error), the requirements are quite consistent with the Semantic Web assumptions and can be modeled easily with a simple pattern. The support that a model provides for question answering is given formally by the inferences that the model entails. As far as an inference engine is concerned, entities in the model could have any name at all, like G0001 or Node97. But names of this sort are of little help when perusing a model to determine whether it can satisfy one's own goals.