Comparing Index Structures for Completeness Reasoning
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Data quality is a major issue in the devel- opment of knowledge graphs. Data completeness is a key factor in data quality pertaining to how broad and deep is information contained in knowledge graphs. As for large- scale knowledge graphs (e.g., DBpedia, Wikidata), it is conceivable that given the vast amount of information contained in there, they may be complete for a wide range of topics, such as children of Joko Widodo, cantons of Switzerland, and presidents of Indonesia. Previous research has shown how one can augment knowledge graphs with statements about their completeness, stating which parts of data are complete. Such meta-information can be leveraged to check query completeness, that is, whether the answer returned by a query is complete. Yet, it is still unclear how such a check can be done in practice, especially when many completeness statements are involved. We devise implementation techniques to make completeness reasoning in the presence of large sets of completeness statements feasible, and experimentally evaluate their effectiveness in realistic settings based on the characteristics of real-world knowledge graphs.