Message Passing Query Embedding
Table 2: Generalization results on complex query answering (AUC) when training for simple link prediction. The results show the performance on a test set of queries with chains only (ch), and the complete set of queries with chains and intersections (all). Dashes indicate not better than random results.
['Method', 'AIFB ch', 'AIFB all', 'MUTAG ch', 'MUTAG all', 'AM ch', 'AM all', 'Bio ch', 'Bio all']
Unlike MPQE, in this case GQE cannot provide an answer better than random for queries with intersections, because the intersection operator is not optimized. We thus consider two evaluations modes: evaluating on queries with chain structures only, and evaluating on the complete set of query structures (where GQE is not applicable). The results of MPQE are competitive when evaluating on queries with chains only, and crucially, it also generalizes well to query structures not seen during training. This encouraging results shows that MPQE implements a mechanism that does not necessarily require training on many diverse query structures to generalize well, unlike GQE.