HyperMono: A Monotonicity-aware Approach to Hyper-Relational Knowledge Representation
<|reference_start|>HyperMono: A Monotonicity-aware Approach to Hyper-Relational Knowledge Representation: In a hyper-relational knowledge graph (HKG), each fact is composed of a main triple associated with attribute-value qualifiers, which express additional factual knowledge. The hyper-relational knowledge graph completion (HKGC) task aims at inferring plausible missing links in a HKG. Most existing approaches to HKGC focus on enhancing the communication between qualifier pairs and main triples, while overlooking two important properties that emerge from the monotonicity of the hyper-relational graphs representation regime. Stage Reasoning allows for a two-step reasoning process, facilitating the integration of coarse-grained inference results derived solely from main triples and fine-grained inference results obtained from hyper-relational facts with qualifiers. In the initial stage, coarse-grained results provide an upper bound for correct predictions, which are subsequently refined in the fine-grained step. More generally, Qualifier Monotonicity implies that by attaching more qualifier pairs to a main triple, we may only narrow down the answer set, but never enlarge it. This paper proposes the HyperMono model for hyper-relational knowledge graph completion, which realizes stage reasoning and qualifier monotonicity. To implement qualifier monotonicity HyperMono resorts to cone embeddings. Experiments on three real-world datasets with three different scenario conditions demonstrate the strong performance of HyperMono when compared to the SoTA.<|reference_end|>
@article{hu2024hypermono:,
    title={HyperMono: A Monotonicity-aware Approach to Hyper-Relational Knowledge
  Representation},
    author={Zhiwei Hu, V'ictor Guti'errez-Basulto, Zhiliang Xiang, Ru Li, Jeff
  Z. Pan},
    journal={arXiv preprint arXiv:2404.09848},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.09848},
    primaryClass={cs.AI cs.LG}
}