Inductive Link Prediction
19 papers with code • 3 benchmarks • 3 datasets
In inductive link prediction inference is performed on a new, unseen graph whereas classical transductive link prediction performs both training and inference on the same graph.
Benchmarks
These leaderboards are used to track progress in Inductive Link Prediction
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ILPC22-Small
Most implemented papers
RAILD: Towards Leveraging Relation Features for Inductive Link Prediction In Knowledge Graphs
genetasefa/raild
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In this work, a novel Relation Aware Inductive Link preDiction (RAILD) is proposed for KG completion which learns representations for both unseen entities and unseen relations.
IRT2: Inductive Linking and Ranking in Knowledge Graphs of Varying Scale
Our focus is on inductive link prediction models as a basis for practical tools that support knowledge engineers with exploring text collections and discovering and linking new (so-called open-world) entities to the knowledge graph.
Double Equivariance for Inductive Link Prediction for Both New Nodes and New Relation Types
We then show how double-equivariant architectures are able to self-supervise pre-train on distinct KG domains and zero-shot predict links on a new KG domain (with completely new entities and new relation types).
Towards Few-shot Inductive Link Prediction on Knowledge Graphs: A Relational Anonymous Walk-guided Neural Process Approach
leapxcheng/rawnp
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Previous methods are limited to transductive scenarios, where entities exist in the knowledge graphs, so they are unable to handle unseen entities.
Disentangling Node Attributes from Graph Topology for Improved Generalizability in Link Prediction
Our proposed method, UPNA (Unsupervised Pre-training of Node Attributes), solves the inductive link prediction problem by learning a function that takes a pair of node attributes and predicts the probability of an edge, as opposed to Graph Neural Networks (GNN), which can be prone to topological shortcuts in graphs with power-law degree distribution.
Event Prediction using Case-Based Reasoning over Knowledge Graphs
To generalize our methods beyond the domain of event prediction, we frame our task as a 2-hop LP task, where the first hop is a causal relation connecting a cause event to a new effect event and the second hop is a property about the new event which we wish to predict.
A Benchmark for Semi-Inductive Link Prediction in Knowledge Graphs
Semi-inductive link prediction (LP) in knowledge graphs (KG) is the task of predicting facts for new, previously unseen entities based on context information.
Towards Enhancing Relational Rules for Knowledge Graph Link Prediction
To address these issues, we propose a novel knowledge graph reasoning approach, the Relational rUle eNhanced Graph Neural Network (RUN-GNN).
Multi-Label Zero-Shot Product Attribute-Value Extraction
We propose HyperPAVE, a multi-label zero-shot attribute value extraction model that leverages inductive inference in heterogeneous hypergraphs.
