A Benchmark for Semi-Inductive Link Prediction in Knowledge Graphs
This provides a test bed for researchers working on knowledge graph completion, but it is incremental as it focuses on benchmarking rather than new methods.
The paper tackles the problem of semi-inductive link prediction in knowledge graphs by proposing a large-scale benchmark based on Wikidata5M, and finds that current models perform far worse than transductive methods, especially on long-tail entities.
Semi-inductive link prediction (LP) in knowledge graphs (KG) is the task of predicting facts for new, previously unseen entities based on context information. Although new entities can be integrated by retraining the model from scratch in principle, such an approach is infeasible for large-scale KGs, where retraining is expensive and new entities may arise frequently. In this paper, we propose and describe a large-scale benchmark to evaluate semi-inductive LP models. The benchmark is based on and extends Wikidata5M: It provides transductive, k-shot, and 0-shot LP tasks, each varying the available information from (i) only KG structure, to (ii) including textual mentions, and (iii) detailed descriptions of the entities. We report on a small study of recent approaches and found that semi-inductive LP performance is far from transductive performance on long-tail entities throughout all experiments. The benchmark provides a test bed for further research into integrating context and textual information in semi-inductive LP models.