{'fraction_non_alphanumeric': 0.013282732447817837, 'fraction_numerical': 0.0, 'mean_word_length': 5.512345679012346, '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}}
Using linguistic techniques for schema matching

In order to deal with the problem of semantic and schematic heterogeneity in collaborative networks, matching components among database schemas need to be identified and heterogeneity needs to be resolved, by creating the corresponding mappings in a process called schema matching. One important step in this process is the identification of the syntactic and semantic similarity among elements from different schemas, usually referred to as Linguistic Matching. The Linguistic Matching component of a schema matching and integration system, called SASMINT, is the focus of this paper. Unlike other systems, which typically utilize only a limited number of similarity metrics, SASMINT makes an effective use of NLP techniques for the Linguistic Matching and proposes a weighted usage of several syntactic and semantic similarity metrics. Since it is not easy for the user to determine the weights, SASMINT provides a component called Sampler as another novelty, to support automatic generation of weights.