Two supervised learning approaches for name disambiguation in author citations
Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
Name disambiguation in author citations using a K-way spectral clustering method
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
A Heterogeneous Field Matching Method for Record Linkage
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Domain-independent data cleaning via analysis of entity-relationship graph
ACM Transactions on Database Systems (TODS)
Duplicate Records Cleansing with Length Filtering and Dynamic Weighting
SKG '08 Proceedings of the 2008 Fourth International Conference on Semantics, Knowledge and Grid
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With the quick development of the semantic web technology, RDF data explosion has become a challenging problem. Since RDF data are always from different resources which may have overlap with each other, they could have duplicates. These duplicates may cause ambiguity and even error in reasoning. However, attentions are seldom paid to this problem. In this paper, we study the problem and give a solution, named K-radius subgraph comparison (KSC). The proposed method is based on RDF-Hierarchical Graph Model. KSC combines similar and comparison of context to detect duplicate in RDF data. Experiments on publication datasets show that the proposed method is efficient in duplicate detection of RDF data. KSC is simpler and less time-costs than other methods of graph comparison.