Lessons Learned — The Case of CROCUS: Cluster-Based Ontology Data Cleansing
"Lessons Learned — The Case of CROCU..." refers methods in this paper
...As a third step, we apply the density-based spatial clustering of applications with noise (DBSCAN) algorithm  since it is an efficient algorithm and the order of instances has no influence on the clustering result....
"Lessons Learned — The Case of CROCU..." refers background in this paper
...Table 4 lists the identified reasons of errors from the German universities DBpedia subset detected as outlier....
...However, CROCUS achieves a high recall on the real-world data from DBpedia....
...To evaluate the performance of CROCUS, we used each error type individually on the adjusted LUBM benchmark datasets as well as a combination of all error types on LUBM5 and the real-world DBpedia subset....
...Often, mature ontologies, grown over years, edited by a large amount of processes and people, created by a third party provide the basis for industrial applications (e.g., DBpedia)....
...In 2013, Zaveri et al.  evaluate the data quality of DBpedia....
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