An epistemic approach to model uncertainty in data-graphs
نویسندگان
چکیده
Graph databases are becoming widely successful as data models that allow to effectively represent and process complex relationships among various types of data. As with any other type repository, graph may suffer from errors discrepancies respect the real-world they intend represent. In this work we explore notion probabilistic unclean databases, previously proposed for relational in order capture idea observed (unclean) database is actually noisy version a clean one correctly world but know partially. factors be involved observation can many, e.g, all different clerical or unintended transformations data, assume model describes distribution over possible ways which (uncertain) could have been polluted. Based on define two computational problems: cleaning query answering study both them their corresponding complexity when considering transformation caused by either removing (subset) adding (superset) nodes edges.
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ژورنال
عنوان ژورنال: International Journal of Approximate Reasoning
سال: 2023
ISSN: ['1873-4731', '0888-613X']
DOI: https://doi.org/10.1016/j.ijar.2023.108948