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

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 fact-wise confidence is straightforward to evaluate. However, hyper-relational 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 \underlineN oise-resistant h\underlineY per-re\underlineL ati\underlineON al 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 …