Uncertain knowledge graphs (UKGs) associate each triple with a confidence score to provide more precise knowledge representations. Recently, since real-world UKGs suffer from the incompleteness, uncertain knowledge graph (UKG) completion attracts more attention, aiming to complete missing triples and confidences. Current studies attempt to learn UKG embeddings to solve this problem, but they neglect the extremely imbalanced distributions of triple confidences. This causes that the learnt embeddings are insufficient to high-quality UKG completion. Thus, in this paper, to address the above issue, we propose a new semi-supervised Confidence Distribution Learning (ssCDL) method for UKG completion, where each triple confidence is transformed into a confidence distribution to introduce more supervision information of different confidences to reinforce the embedding learning process. ssCDL iteratively learns UKG embedding by relational learning on labeled data (i.e., existing triples with confidences) and unlabeled data with pseudo labels (i.e., unseen triples with the generated confidences), which are predicted by meta-learning to augment the training data and rebalance the distribution of triple confidences. Experiments on two UKG datasets demonstrate that ssCDL consistently outperforms the state-of-the-art baselines in different evaluation metrics.

Recently, uncertain knowledge graphs (UKGs), where each relation between entities is associated with a confidence score, have gained much attention. Compared with traditional knowledge graphs, UKGs possess the capability of uncertainty knowledge expression, which facilitates more reliable and precise knowledge graph reasoning by not only completing missing triples but also predicting triple confidences. In this paper, we release unKR, the first open-source python library for uncertain Knowledge graph (UKG) Reasoning by representation learning. We design a unified framework to implement two types of representation learning models for UKG reasoning, i.e., normal and few-shot ones. Besides, we standardize the evaluation tasks and metrics for UKG reasoning to ensure fair comparisons, and report the detailed results of each model under the consistent test setting. With unKR, it is effortless for users to reproduce existing models, as well as efficiently customize their own models. The library, documentation, demo, and re-implementing results are all publicly released at https://github.com/seucoin/unKR.