Papers Inductive Relation Prediction
“Inductive Relation Prediction” 태그가 달린 논문 14편 · 필터 해제
Learning Rule-Induced Subgraph Representations for Inductive Relation Prediction
Inductive relation prediction (IRP) -- where entities can be different during training and inference -- has shown great power for completing evolving knowledge graphs. Existing works mainly focus on using graph neural ne…
Inductive Relation PredictionKnowledge GraphsPredictionRelation+1Anchoring Path for Inductive Relation Prediction in Knowledge Graphs
Aiming to accurately predict missing edges representing relations between entities, which are pervasive in real-world Knowledge Graphs (KGs), relation prediction plays a critical role in enhancing the comprehensiveness a…
Inductive Relation PredictionKnowledge GraphsPredictionRelation+4Extending Transductive Knowledge Graph Embedding Models for Inductive Logical Relational Inference
Many downstream inference tasks for knowledge graphs, such as relation prediction, have been handled successfully by knowledge graph embedding techniques in the transductive setting. To address the inductive setting wher…
Graph EmbeddingGraph Neural NetworkInductive Relation PredictionKnowledge Graph Completion+3Inductive Relation Prediction from Relational Paths and Context with Hierarchical Transformers
Relation prediction on knowledge graphs (KGs) is a key research topic. Dominant embedding-based methods mainly focus on the transductive setting and lack the inductive ability to generalize to new entities for inference.…
Inductive Relation PredictionKnowledge GraphsRelationRelation PredictionMulti-Aspect Explainable Inductive Relation Prediction by Sentence Transformer
Recent studies on knowledge graphs (KGs) show that path-based methods empowered by pre-trained language models perform well in the provision of inductive and explainable relation predictions. In this paper, we introduce …
Inductive Relation PredictionKnowledge GraphsPredictionRelation+2Relation-dependent Contrastive Learning with Cluster Sampling for Inductive Relation Prediction
Relation prediction is a task designed for knowledge graph completion which aims to predict missing relationships between entities. Recent subgraph-based models for inductive relation prediction have received increasing …
Contrastive LearningInductive Relation PredictionKnowledge Graph CompletionPrediction+3Meta-Knowledge Transfer for Inductive Knowledge Graph Embedding
Knowledge graphs (KGs) consisting of a large number of triples have become widespread recently, and many knowledge graph embedding (KGE) methods are proposed to embed entities and relations of a KG into continuous vector…
Entity EmbeddingsGraph EmbeddingInductive Relation PredictionKnowledge Graph Embedding+6Cycle Representation Learning for Inductive Relation Prediction
In recent years, algebraic topology and its modern development, the theory of persistent homology, has shown great potential in graph representation learning. In this paper, based on the mathematics of algebraic topology…
Graph Representation LearningInductive Relation PredictionKnowledge Graph CompletionKnowledge Graphs+4A Topological View of Rule Learning in Knowledge Graphs
Inductive relation prediction is an important learning task for knowledge graph completion. One can use the existence of rules, namely a sequence of relations, to predict the relation between two entities. Previous works…
Inductive Relation PredictionKnowledge Graph CompletionKnowledge GraphsRelation+1Inductive Relation Prediction Using Analogy Subgraph Embeddings
Prevailing methods for relation prediction in heterogeneous graphs aim at learning latent representations (i.e., embeddings) of observed nodes and relations, and thus are limited to the transductive setting where the rel…
Inductive BiasInductive Relation PredictionKnowledge Graph CompletionPrediction+2Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction
Link prediction is a very fundamental task on graphs. Inspired by traditional path-based methods, in this paper we propose a general and flexible representation learning framework based on paths for link prediction. Spec…
Graph Neural NetworkInductive Relation PredictionKnowledge Graph CompletionLink Prediction+1Inductive Relation Prediction by BERT
Relation prediction in knowledge graphs is dominated by embedding based methods which mainly focus on the transductive setting. Unfortunately, they are not able to handle inductive learning where unseen entities and rela…
Few-Shot LearningInductive LearningInductive Relation PredictionKnowledge Graphs+6Communicative Message Passing for Inductive Relation Reasoning
Relation prediction for knowledge graphs aims at predicting missing relationships between entities. Despite the importance of inductive relation prediction, most previous works are limited to a transductive setting and c…
Inductive BiasInductive Relation PredictionKnowledge GraphsRelation+2Inductive Relation Prediction by Subgraph Reasoning
The dominant paradigm for relation prediction in knowledge graphs involves learning and operating on latent representations (i.e., embeddings) of entities and relations. However, these embedding-based methods do not expl…
Graph EmbeddingGraph Neural NetworkInductive BiasInductive knowledge graph completion+9