Robust Link Prediction over Noisy Hyper-Relational Knowledge Graphs via Active Learning

Abstract

Modern Knowledge Graphs (KGs) are inevitably noisy due to the nature of their construction process. Existing robust learning techniques for noisy KGs mostly focus on triple facts, where the factwise confidence is straightforward to evaluate. However, hyperrelational facts, where an arbitrary number of key-value pairs are associated with a base triplet, have become increasingly popular in modern KGs, but significantly complicate the confidence assessment of the fact. Against this background, we study the problem of robust link prediction over noisy hyper-relational KGs, and propose NYLON, a Noise-resistant hYper-reLatiONal link prediction technique via active crowd learning. Specifically, beyond the traditional fact-wise confidence, we first introduce element-wise confidence measuring the fine-grained confidence of each entity or relation of a hyper-relational fact. We connect the element- and fact-wise confidences via a “least confidence” principle to allow efficient crowd labeling. NYLON is then designed to systematically integrate three key components, where a hyper-relational link predictor uses the fact-wise confidence for robust prediction, a cross-grained confidence evaluator predicts both element- and fact-wise confidences, and an effort-efficient active labeler selects informative facts for crowd annotators to label using an efficient labeling mechanism guided by the element-wise confidence under the “least confidence” principle and further followed by data augmentation. We evaluate NYLON on three real-world KG datasets against a sizeable collection of baselines. Results show that NYLON achieves superior and robust performance in both link prediction and error detection tasks on noisy KGs, and outperforms best baselines by 2.42-10.93% and 3.46-10.65% in the two tasks, respectively.