Query2box: Reasoning over KnowledgeGraphs in Vector Space using Box Embeddings
Table 2: H@3 results of query2box vs. gqe on FB15k, FB15k-237 and NELL995.
['[BOLD] Method', '[BOLD] Avg', '[BOLD] 1p', '[BOLD] 2p', '[BOLD] 3p', '[BOLD] 2i', '[BOLD] 3i', '[BOLD] ip', '[BOLD] pi', '[BOLD] 2u', '[BOLD] up']
On average, we obtain 9.8% (25% relative), 3.8% (15% relative), and 5.9% (24% relative) higher H@3 than the best baselines on FB15k, FB15k-237, and NELL995, respectively. Notice that naïvely increasing embedding dimensionality in gqe yields limited performance improvement. Our q2b is able to effectively model a large set of entities by using the box embedding, and achieves a significant performance gain compared with gqe-double (with same number of parameters) that represents queries as point vectors. Also notice that q2b performs well on new queries with the same structure as the training queries as well as on new query structures never seen during training, which demonstrates that q2b generalizes well within and beyond query structures.