TY - GEN
T1 - Comparing Index Structures for Completeness Reasoning
AU - Darari, Fariz
AU - Nutt, Werner
AU - Razniewski, Simon
N1 - Funding Information:
ACKNOWLEDGMENTS This work was partially supported by TaDaQua, funded by the Free University of Bolzano, Italy.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/9/24
Y1 - 2018/9/24
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85055542970&partnerID=8YFLogxK
U2 - 10.1109/IWBIS.2018.8471712
DO - 10.1109/IWBIS.2018.8471712
M3 - Conference contribution
AN - SCOPUS:85055542970
T3 - 2018 International Workshop on Big Data and Information Security, IWBIS 2018
SP - 49
EP - 56
BT - 2018 International Workshop on Big Data and Information Security, IWBIS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 International Workshop on Big Data and Information Security, IWBIS 2018
Y2 - 12 May 2018 through 13 May 2018
ER -