InductWave: Inductive Multi-Hop Logical Query Answering on Knowledge Graphs
This work addresses the problem of inductive logical query answering for large-scale knowledge graphs, offering a resource-efficient solution that outperforms existing models.
InductWave introduces a wavelet-based inductive embedding method for multi-hop logical query answering on knowledge graphs, enabling reasoning over unseen entities. It achieves state-of-the-art performance on FB15k-237 with 75% fewer message-passing layers, and scales to large graphs like Wiki-KG.
Logical Multi-Hop Query Answering over Knowledge Graphs (KGs) can be formulated as querying, with an implicit completeness assumption. Current works mainly focus on Existential First Order Logic (EFO) queries. These EFO queries contain conjunction, disjunction, and negation operators. Most existing works employ transductive reasoning, meaning they are not capable of reasoning over entities unseen during training. In the real world, there is a resource scarcity, and we cannot train a model with all the nodes of a large KG. Hence, we propose InductWave, a wavelet-based inductive embedding method for logical query answering on large KGs. Here, the training graph consists of fewer nodes than the test graph. Our model performs on par with the baseline models while having half the number of message-passing layers. It outperforms all of them in most cases, with 75% of the layers. These fewer resource requirements enable us to evaluate InductWave on massive graphs, such as Wiki-KG. We test our model using extensive experiments across varying train-test graph proportions of the FB15k-(237) dataset, comparing it with the state-of-the-art models. The code and datasets for the model are available at https://github.com/kracr/inductwave/.