LP-4P: Link Prediction over Annotated Knowledge Graphs with Four Patterns

Initially designed for Knowledge Graphs (KG) containing plain (s, p, o) triples, research on link prediction has evolved to address more complex structures such as hypergraphs, hyper-relational graphs, and bi-level graphs. These advancements push link prediction to handle annotation patterns where a triple is annotated with another (t, p, t), or where a triple is annotated with qualifiers (t, p, o). However, each approach focuses on a single annotation pattern, and the case where a triple is annotated with a subject (s, p, t) has never been explored. In this paper, we propose LP4P, the first link prediction model capable of predicting entities in both plain triples and three annotation patterns. LP4P captures the information expressed in annotations through a four-pattern attention layer, and its loss function further leverages the ontological information of KGs. Moreover, we built WD4P, an RDF KG derived from baseline datasets, which includes the four patterns. Extensive empirical evaluations demonstrate that LP4P outperforms relevant state-of-the-art models on knowledge graphs with the four patterns, and achieves comparable performance on standard benchmarks.