Papers Inductive knowledge graph completion
“Inductive knowledge graph completion” 태그가 달린 논문 21편 · 필터 해제
Cumulative Path-Level Semantic Reasoning for Inductive Knowledge Graph Completion
Conventional Knowledge Graph Completion (KGC) methods aim to infer missing information in incomplete Knowledge Graphs (KGs) by leveraging existing information, which struggle to perform effectively in scenarios involving…
Inductive knowledge graph completionKnowledge GraphsS$^2$DN: Learning to Denoise Unconvincing Knowledge for Inductive Knowledge Graph Completion
Inductive Knowledge Graph Completion (KGC) aims to infer missing facts between newly emerged entities within knowledge graphs (KGs), posing a significant challenge. While recent studies have shown promising results in in…
DenoisingInductive knowledge graph completionKnowledge Graph CompletionKnowledge GraphsContext-aware Inductive Knowledge Graph Completion with Latent Type Constraints and Subgraph Reasoning
Inductive knowledge graph completion (KGC) aims to predict missing triples with unseen entities. Recent works focus on modeling reasoning paths between the head and tail entity as direct supporting evidence. However, the…
Inductive knowledge graph completionKnowledge Graph CompletionTowards Better Benchmark Datasets for Inductive Knowledge Graph Completion
Knowledge Graph Completion (KGC) attempts to predict missing facts in a Knowledge Graph (KG). Recently, there's been an increased focus on designing KGC methods that can excel in the {\it inductive setting}, where a port…
Inductive knowledge graph completionKnowledge Graph CompletionQuery-Enhanced Adaptive Semantic Path Reasoning for Inductive Knowledge Graph Completion
Conventional Knowledge graph completion (KGC) methods aim to infer missing information in incomplete Knowledge Graphs (KGs) by leveraging existing information, which struggle to perform effectively in scenarios involving…
Inductive knowledge graph completionKnowledge Graph CompletionKnowledge GraphsLogical Reasoning with Relation Network for Inductive Knowledge Graph Completion
Inductive knowledge graph completion (KGC) aims to infer the missing relation for a set of newly-coming entities that never appeared in the training set. Such a setting is more in line with reality, as real-world KGs are…
Inductive knowledge graph completionKnowledge Graph CompletionLogical ReasoningRelation+1Predicting from a Different Perspective: A Re-ranking Model for Inductive Knowledge Graph Completion
Rule-induction models have demonstrated great power in the inductive setting of knowledge graph completion. In this setting, the models are tested on a knowledge graph entirely composed of unseen entities. These models l…
Inductive knowledge graph completionKnowledge Graph CompletionLink PredictionRe-RankingOne Subgraph for All: Efficient Reasoning on Opening Subgraphs for Inductive Knowledge Graph Completion
Knowledge Graph Completion (KGC) has garnered massive research interest recently, and most existing methods are designed following a transductive setting where all entities are observed during training. Despite the great…
AllGraph ClassificationInductive knowledge graph completionKnowledge Graph CompletionInductive Knowledge Graph Completion with GNNs and Rules: An Analysis
The task of inductive knowledge graph completion requires models to learn inference patterns from a training graph, which can then be used to make predictions on a disjoint test graph. Rule-based methods seem like a natu…
Inductive knowledge graph completionKnowledge Graph CompletionLink PredictionInGram: Inductive Knowledge Graph Embedding via Relation Graphs
Inductive knowledge graph completion has been considered as the task of predicting missing triplets between new entities that are not observed during training. While most inductive knowledge graph completion methods assu…
Entity EmbeddingsGraph EmbeddingInductive knowledge graph completionInductive Learning+5Bi-Link: Bridging Inductive Link Predictions from Text via Contrastive Learning of Transformers and Prompts
Inductive knowledge graph completion requires models to comprehend the underlying semantics and logic patterns of relations. With the advance of pretrained language models, recent research have designed transformers for …
Contrastive LearningEntity LinkingInductive knowledge graph completionKnowledge Graph Completion+3Relational Message Passing for Fully Inductive Knowledge Graph Completion
In knowledge graph completion (KGC), predicting triples involving emerging entities and/or relations, which are unseen when the KG embeddings are learned, has become a critical challenge. Subgraph reasoning with message …
Inductive knowledge graph completionKnowledge Graph CompletionRelationStATIK: Structure and Text for Inductive Knowledge Graph Completion
Knowledge graphs (KGs) often represent knowledge bases that are incomplete. Machine learning models can alleviate this by helping automate graph completion. Recently, there has been growing interest in completing knowled…
Inductive knowledge graph completionKnowledge Graph CompletionKnowledge GraphsINDIGO: GNN-Based Inductive Knowledge Graph Completion Using Pair-Wise Encoding
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…
Inductive knowledge graph completionKnowledge Graph CompletionInductive Entity Representations from Text via Link Prediction
Knowledge Graphs (KG) are of vital importance for multiple applications on the web, including information retrieval, recommender systems, and metadata annotation. Regardless of whether they are built manually by domain e…
Inductive knowledge graph completionInductive Link PredictionInformation RetrievalKnowledge Graph Embeddings+8Building Rule Hierarchies for Efficient Logical Rule Learning from Knowledge Graphs
Many systems have been developed in recent years to mine logical rules from large-scale Knowledge Graphs (KGs), on the grounds that representing regularities as rules enables both the interpretable inference of new facts…
Inductive knowledge graph completionKnowledge GraphsTowards Learning Instantiated Logical Rules from Knowledge Graphs
Efficiently inducing high-level interpretable regularities from knowledge graphs (KGs) is an essential yet challenging task that benefits many downstream applications. In this work, we present GPFL, a probabilistic rule …
Inductive knowledge graph completionKnowledge Graph CompletionKnowledge GraphsInductive 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+9KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation
Pre-trained language representation models (PLMs) cannot well capture factual knowledge from text. In contrast, knowledge embedding (KE) methods can effectively represent the relational facts in knowledge graphs (KGs) wi…
Entity EmbeddingsEntity TypingInductive knowledge graph completionKnowledge Graph Completion+7DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs
In this paper, we study the problem of learning probabilistic logical rules for inductive and interpretable link prediction. Despite the importance of inductive link prediction, most previous works focused on transductiv…
Inductive knowledge graph completionInductive Link PredictionKnowledge GraphsLink Prediction+1