An Open-World Extension to Knowledge Graph Completion Models
Table 4: Open-world tail prediction by applying the transformation to different closed-world link prediction models on the FB15k-237-OWE dataset without target filtering.
['Model', 'MRR Filt.', 'MRR Raw', 'HITS@ 1', 'HITS@ 3', 'HITS@ 10']
Our OWE extension for open-world link prediction can be used with any common KGC model. Therefore, we evaluate three commonly used options, namely TransE, DistMult, and ComplEx. For text embeddings, Wikipedia2Vec embeddings of the same dimensionality were used. It can be seen that the performance on the open-world setting matches the expressiveness of the models: ComplEx-OWE with its ability to model even asymmetric relations yields the best results, while the symmetric DistMult-OWE achieves a similar performance.