Open-World Knowledge Graph Completion
Table 1: Open-world Entity prediction results on DBPedia50k and DBPedia500k. For Mean Rank (MR) lower is better. For HITS@10 and Mean Reciprocal Rank (MRR) higher is better.
['[EMPTY]', 'DBPedia50k Head', 'DBPedia50k Head', 'DBPedia50k Head', 'DBPedia50k Tail', 'DBPedia50k Tail', 'DBPedia50k Tail', 'DBPedia500k Head', 'DBPedia500k Head', 'DBPedia500k Head', 'DBPedia500k Tail', 'DBPedia500k Tail', 'DBPedia500k Tail']
To mitigate the excessive cost involved in computing scores for all entities in the KG, we applied a target filtering method to all KGC models. Namely, for a given partial triple ⟨h, r, ? ⟩ or ⟨?, r, t⟩, if a target entity candidate has not been connected via relationship r before in the training set , then it is skipped, otherwise we use the KGC model to calculate the actual ranking score. Simply put, this removes relationship-entity combinations that have never before been seen and are likely to represent nonsensical statements. The experiment results are shown in Tab. As a naive baseline we include the target filtering baseline method in Tab. Semantic Averaging is a simplified model which uses contextual features only. We implemented DKRL ourselves and removed the structural-related features so it can work under open-world KGC settings.