{'fraction_non_alphanumeric': 0.015368852459016393, 'fraction_numerical': 0.005122950819672131, 'mean_word_length': 4.850299401197605, '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}}
Time-efficient execution of bounded Jaro-Winkler distances

Over the last years, time-efficient approaches for the discovery of links between knowledge bases have been regarded as a key requirement towards implementing the idea of a Data Web. A considerable portion of the information contained available as RDF on the Web pertains to persons. Thus, efficient and effective measures for comparing names are central to facilitate the integration of information about persons on the Web of Data. The Jaro-Winkler measure has been developed especially for the purpose of comparing person names. Hence, we present a novel approach for the efficient comparison of sets of strings using this measure. We evaluate our approach on several datasets derived from DBpedia 3.9 and containing up to 105 strings and show that it scales linearly with the size of the data for large thresholds. We also evaluate our approach against SILK and show that we outperform it even on small datasets.