Factual knowledge graphs (KGs) such as DBpedia and Wikidata have served as part of various downstream tasks and are also widely adopted by artificial intelligence research communities as benchmark datasets. However, we found those KGs to be surprisingly noisy. In this study, we question the quality of these KGs, where the typing error rate is estimated to be 27% for coarse-grained types on average, and even 73% for certain fine-grained types. In pursuit of solutions, we propose an active typing error detection algorithm that maximizes the utilization of both gold and noisy labels. We also comprehensively discuss and compare unsupervised, semi-supervised, and supervised paradigms to deal with typing errors in factual KGs. The outcomes of this study provide guidelines for researchers to use noisy factual KGs, and we will also publish our code and data alone with the paper to help practitioners deploy the techniques and conduct further research.

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