INDIGO: GNN-based inductive knowledge graph completion using pair-wise encoding

Conference item

The aim of knowledge graph (KG) completion is to extend an incomplete KG with missing triples. Popular approaches based on graph embeddings typically work by first representing the KG in a vector space, and then applying a predefined scoring function to the resulting vectors to complete the KG. These approaches work well in transductive settings, where predicted triples involve only constants seen during training; however, they are not applicable in inductive settings, where the KG on which the model was trained is extended with new constants or merged with other KGs. The use of Graph Neural Networks (GNNs) has recently been proposed as a way to overcome these limitations; however, existing approaches do not fully exploit the capabilities of GNNs and still rely on heuristics and ad-hoc scoring functions. In this paper, we propose a novel approach, where the KG is fully encoded into a GNN in a transparent way, and where the predicted triples can be read out directly from the last layer of the GNN without the need for additional components or scoring functions. Our experiments show that our model outperforms state-of-the-art approaches on inductive KG completion benchmarks.

Authors: Liu, S; Grau, B; Horrocks, I; Kostylev, EV

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Neural Information Processing Systems Foundation
• Host title: Advances in Neural Information Processing Systems 34 (NeurIPS 2021)
• Publication date: 2021-12-14
• Acceptance date: 2021-09-28
• Event title: 35th Conference on Neural Information Processing Systems (NeurIPS 2021)
• Event location: Virtual event
• Event website: https://neurips.cc/Conferences/2021
• Event start date: 2021-12-06
• Event end date: 2021-12-14
• Language: English
• Keywords: FFR