Enhancing Knowledge Graph Completion with Positive Unlabeled Learning

Knowledge graphs have proven to be incredibly useful for many artificial intelligence applications. Although typical knowledge graphs may contain a huge amount of facts, they are far from being complete, which motivates an increasing research interest in learning statistical models for knowledge graph completion. Learning such models relies on sampling appropriate number of negative examples, as only the positive examples are contained in the data set. However, this would introduce errors or heuristic biases which restrict the sampler to visit other potentially reliable negative examples for better prediction models. In this paper, we present a novel perspective on skillfully selecting the negative examples for knowledge graph completion. We develop a two-stage logistic regression filter under the positive-unlabeled learning (PU learning) framework, which enables an automatic and iterative refinement of the negative candidate pools. We then contrast positive examples with the resulting negative ones based on the improved embedding-based models. In particular, we work with a cost-sensitive loss function by weighting the semantic differences between negative examples and particular positive ones. This weighting scheme reflects the importance of predicting the preferences between them correctly. In experiments, we validate the effectiveness of negative examples in refining and weighting schemes, respectively. Besides this, our proposed prediction model also outperforms the state-of-the-art methods on two public datasets.