Analyzing Inductive Knowledge Graph Completion with GNNs and Rules
Rule-based methods underperform due to limitations in ranking implausible entities and aggregating evidence, which can be addressed by integrating GNN strategies.
Efficient Reasoning on Opening Subgraphs for Inductive Knowledge Graph Completion
The proposed global-local anchor representation (GLAR) learning method can efficiently perform inductive reasoning on opening subgraphs and learn rich entity-independent features for emerging entities in knowledge graphs.
Efficient Knowledge Graph Completion without Explicit Path Encoding
A Transformer-based model with connection-biased attention and entity role embeddings can effectively perform knowledge graph completion without the need for explicit path encoding.
The Problem of Shortcuts in Inductive Knowledge Graph Completion Datasets and a Proposed Solution Using Graph Partitioning
Current inductive knowledge graph completion datasets suffer from a shortcut where Personalized PageRank (PPR) can achieve high performance by exploiting differences in shortest path distances between entities, hindering the accurate evaluation of inductive reasoning capabilities of KGC models. This paper proposes a new dataset construction method using graph partitioning to mitigate this shortcut and provide more reliable benchmarks for inductive KGC.
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