Representation Learning Based Predicate Invention on Knowledge Graphs

The recognition of whether or not a predicate should be invented is an important problem in the domain of predicate invention. Despite its significance, existing research has yet to fully harness the rich data available in knowledge graphs. In this paper, we introduce a novel problem formulation, ReLPI (Representation Learning for Predicate Invention in Knowledge Graphs), which constitutes a significant advancement in this domain. To address the core issues of ReLPI, we devise a scoring function that informs the learning process. By optimizing embeddings towards this scoring function, we endow them with semantic meaning, crucial for capturing the nuances of predicate presence patterns. Furthermore, we present SEmPI (Semantic Embeddings for Predicate Invention), a framework that leverages predicate (relation) embeddings as a trainable medium. SEmPI uncovers latent patterns governing predicate occurrences in knowledge graphs, enabling the invention of novel predicates grounded in these discovered patterns. This approach represents a significant step forward in leveraging data-driven methods for predicate invention in knowledge graphs. We evaluate the proposed approach on FB15k and DRKG datasets, and the results demonstrate the effectiveness of SEmPI in discovering new predicates.