Semantically Enriched Datasets for Link Prediction: DB100k+, NELL-995+ and YAGO3-10+

Résumé :

Knowledge graphs constitute a native neuro-symbolic experimental setting due to their logic foundations, which motivates the development of neuro-symbolic approaches for Link Prediction (LP). Since current LP reference datasets seldom involves ontological knowledge, benchmarking such approaches is difficult. That is why, starting from the widely accepted datasets DB100k, NELL-995 and YAGO3-10, we semantically enriched them with ontological knowledge, namely class hierarchy and relation signatures (domains and ranges), and inferred new entity type assertions to create DB100k+, NELL-995+ and YAGO3-10+. We also present a generic masking script to generate sub-graphs with variable proportions of triples with signed/partially signed (no domain or no range)/unsigned (no domain and no range) relations, to evaluate the impact of semantic information on learning performance.