Abstract
Knowledge graph reasoning in the fully-inductive setting — where both entities and relations at test time are unseen during training — remains an open challenge. We introduce GraphOracle, a novel framework that transforms each knowledge graph into a Relation-Dependency Graph (RDG) encoding directed precedence links between relations. A multi-head attention mechanism produces context-aware relation embeddings that guide inductive message passing over the original KG. Experiments on 60 benchmarks show up to 25% improvement in fully-inductive and 28% in cross-domain scenarios.
Motivation
Existing fully-inductive methods (INGRAM, ULTRA) construct undirected relation graphs that are dense and fail to capture directed compositional patterns — e.g., “born_in → located_in” is a directional dependency that undirected graphs cannot distinguish. Furthermore, they are limited to single-domain scenarios and cannot transfer across entirely different knowledge graphs.
Method
GraphOracle operates in three stages:
- RDG Construction — Transform the KG into a directed Relation-Dependency Graph where edge (r_i, r_j) indicates that relation r_i precedes r_j in observed triple chains. This is significantly sparser than prior relation graphs while capturing compositional patterns.
- Query-Dependent Multi-Head Attention — For each query relation, a multi-head attention GNN propagates messages over the RDG to produce context-aware relation embeddings. The same relation gets different representations depending on the query context.
- Entity-Level Answer Prediction — The learned relation embeddings parameterize a second GNN on the original KG entity graph, performing inductive message passing from the query entity to score candidates.
Experimental Results
Evaluated across 60 benchmarks spanning transductive, entity-inductive, fully-inductive, and cross-domain settings.
MRR Comparison on 60 Datasets
GraphOracle (red) consistently matches or outperforms supervised SOTA (green) across all 60 datasets, with particularly strong gains on cross-domain and biomedical KGs.
Average Performance (4 Settings)
GraphOracle achieves +7.19% MRR improvement in transductive, +10.86% in entity-inductive, +13.28% in fully-inductive, and +26.82% in cross-domain settings over supervised SOTA.
Ablation Study
Removing the RDG structure or multi-head attention causes significant performance drops, confirming both components are essential. The directed precedence encoding is the single most important design choice.