Does William Shakespeare REALLY Write Hamlet? Knowledge Representation Learning with Confidence
Table 3: Evaluation results on triple classification
['Datasets', 'FB15K-N1', 'FB15K-N2', 'FB15K-N3']
We can find that: (1) The CKRL models outperform baseline on all datasets, and the improvements become more significant with higher noise rates. It confirms that learning knowledge representations with triple confidence could also help for triple classification. (2) The advantages confidence-aware models have over baseline in this task seem to be smaller than those in KG noise detection. It is because that the CKRL models concentrate more on calculating confidences for triples in training set, but not for negative triples generated in test set. The possible improvements for CKRL in triple classification can only derive from better-learned knowledge representations, which are less straightforward compared to that in KG noise detection. Although CKRL models learn better knowledge representations, conventional models without confidence may also achieve comparable results.