Repairing Entities using Star Constraints in Multirelational Graphs
Repairing Entities using Star Constraints in Multirelational Graphs
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DOI:
10.1109/icde48307.2020.00027
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发表时间:
2020-04
期刊:
影响因子:
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通讯作者:
Peng Lin;Qi Song;Yinghui Wu;Jiaxing Pi
中科院分区:
文献类型:
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作者:
Peng Lin;Qi Song;Yinghui Wu;Jiaxing Pi
This paper studies a class of neighborhood con-straints to characterize and repair erroneous entity information in multi-relational graph data. (1) We propose a class of constraints called star functional dependencies (StarFDs). Unlike conventional integrity constraints, a StarFDenforces value dependencies conditioned by entities and their relevant neighbors, which are identified by a star pattern that incorporates conjunctive regular path queries. StarFDsachieve a balance between expressiveness and complexity: the validation of StarFDsis tractable, and the satisfiability and implication of StarFDsare NP-complete and coNP-complete, respectively. (2) Given a set of StarFDsΣ and a graph G, the entity repair problem is to compute a minimum repair of G by enforcing Σ with the smallest amount of changes. Although this problem is NP-complete and hard to approximate, we show it is feasible to compute repairs in large graphs. Our approach (a) discriminately detects and resolves errors with optimal, approximable and cost-bounded solutions whenever possible, and (b) incurs a time cost determined by Σ and the size of inconsistencies, for all cases. Using real world data, we show that StarFD-based techniques effectively identify and repair errors. We also show that our repairing algorithms benefit other tasks such as fact checking.