{'fraction_non_alphanumeric': 0.014198782961460446, 'fraction_numerical': 0.0, 'mean_word_length': 5.367741935483871, '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}}
Towards Easy Matching Between Statistical Linked Data: Dimension Patterns

Users of statistics expect linked data technology to make it easy to discover statistical data from different sources that can match each other. We carried out a trial matching between statistical linked data and pointed out the importance of upper concepts concerning dimensions and schema-level description about external linkages. We surveyed statistical linked data endpoints, finding that many dimension descriptions cannot have the information about the upper concepts and the external linkages. It is because they directly use external resources, to which local description cannot be added. Hence, we propose two patterns of dimension description to avoid this problem. So far, only small numbers of upper concepts have been supplied for statistical data. Therefore, the parts of the patterns concerning upper concepts are preparatory for the future and the benefit of the patterns is limited at present.