With the ubiquity of hyper-relational facts in modern Knowledge Graphs (KGs), existing link prediction techniques mostly focus on learning the sophisticated relationships among multiple entities and relations contained in a fact, while ignoring the multimodal information, which often provides additional clues to boost link prediction performance.Nevertheless, traditional multimodal fusion approaches, which are mainly designed for triple facts under either entity-centric or relation-guided fusion schemes, fail to integrate the multimodal information with the rich context of the hyperrelational fact consisting of multiple entities and relations.Against this background, we propose HyperFM, a Hyper-relational Factcentric Multimodal Fusion technique.It effectively captures the intricate interactions between different data modalities while accommodating the hyper-relational structure of the KG in a fact-centric manner via a customized Hypergraph Transformer.We evaluate Hy-perFM against a sizeable collection of baselines in link prediction tasks on two real-world KG datasets.The results show that HyperFM consistently achieves the best performance, yielding an average improvement of 6.0-6.8% over the best-performing baselines on the two datasets.Moreover, a series of ablation studies systematically validate our fact-centric fusion scheme.

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 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.

Knowledge graph (KG) schema, which prescribes a high-level structure and semantics of a KG, is significantly helpful for KG completion and reasoning problems. Despite its usefulness, open-domain KGs do not practically have a unified and fixed schema. Existing approaches usually extract schema information using entity types from a KG where each entity e can be associated with a set of types {Te, by either heuristically taking one type for each entity or exhaustively combining the types of all entities in a fact (to get entity-typed tuples, (h_type, r, t_type) for example). However, these two approaches either overlook the role of multiple types of a single entity across different facts or introduce non-negligible noise as not all the type combinations actually support the fact, thus failing to capture the sophisticated schema information. Against this background, we study the problem of modeling hyper-relational schema, which is formulated as mixed hyper-relational tuples ({Th}, r, {Tt}, k, {Tv1},...) with two-fold hyper-relations: each type set T may contain multiple types and each schema tuple may contain multiple key-type set pairs (k, Tv). To address this problem, we propose HELIOS, a hyper-relational schema model designed to subtly learn from such hyper-relational schema tuples by capturing not only the correlation between multiple types of a single entity, but also the correlation between types of different entities and relations in a schema tuple. We evaluate HELIOS on three real-world KG datasets in different schema prediction tasks. Results show that HELIOS consistently outperforms state-of-the-art hyper-relational link prediction techniques by 20.0-29.7%, and is also much more robust than baselines in predicting types and relations across different positions in a hyper-relational schema tuple.