Papers Knowledge Graph Completion
“Knowledge Graph Completion” 태그가 달린 논문 541편 · 필터 해제
Few-Shot Inductive Learning on Temporal Knowledge Graphs using Concept-Aware Information
Knowledge graph completion (KGC) aims to predict the missing links among knowledge graph (KG) entities. Though various methods have been developed for KGC, most of them can only deal with the KG entities seen in the trai…
Inductive LearningKnowledge Graph CompletionKnowledge GraphsLink Prediction+2KGLM: Integrating Knowledge Graph Structure in Language Models for Link Prediction
The ability of knowledge graphs to represent complex relationships at scale has led to their adoption for various needs including knowledge representation, question-answering, and recommendation systems. Knowledge graphs…
Fraud DetectionKnowledge Graph CompletionKnowledge GraphsLanguage Modeling+5Search to Pass Messages for Temporal Knowledge Graph Completion
Completing missing facts is a fundamental task for temporal knowledge graphs (TKGs). Recently, graph neural network (GNN) based methods, which can simultaneously explore topological and temporal information, have become …
Graph Neural NetworkKnowledge Graph CompletionKnowledge GraphsLink Prediction+3Bi-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+3PALT: Parameter-Lite Transfer of Language Models for Knowledge Graph Completion
This paper presents a parameter-lite transfer learning approach of pretrained language models (LM) for knowledge graph (KG) completion. Instead of finetuning, which modifies all LM parameters, we only tune a few new para…
Knowledge Graph CompletionLink PredictionTransfer LearningAdapters for Enhanced Modeling of Multilingual Knowledge and Text
Large language models appear to learn facts from the large text corpora they are trained on. Such facts are encoded implicitly within their many parameters, making it difficult to verify or manipulate what knowledge has …
Entity AlignmentKnowledge Graph CompletionKnowledge GraphsJoint Multilingual Knowledge Graph Completion and Alignment
Knowledge graph (KG) alignment and completion are usually treated as two independent tasks. While recent work has leveraged entity and relation alignments from multiple KGs, such as alignments between multilingual KGs wi…
Knowledge Graph CompletionKnowledge GraphsRelationMoSE: Modality Split and Ensemble for Multimodal Knowledge Graph Completion
Multimodal knowledge graph completion (MKGC) aims to predict missing entities in MKGs. Previous works usually share relation representation across modalities. This results in mutual interference between modalities during…
Knowledge Graph CompletionRelationRepresentation LearningAlong the Time: Timeline-traced Embedding for Temporal Knowledge Graph Completion
Recent years have witnessed remarkable progress on knowledge graph embedding (KGE) methods to learn the representations of entities and relations in static knowledge graphs (SKGs). However, knowledge changes over time. I…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingKnowledge Graphs+2Using Graph Algorithms to Pretrain Graph Completion Transformers
Recent work on Graph Neural Networks has demonstrated that self-supervised pretraining can further enhance performance on downstream graph, link, and node classification tasks. However, the efficacy of pretraining tasks …
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingNode ClassificationMICO: A Multi-alternative Contrastive Learning Framework for Commonsense Knowledge Representation
Commonsense reasoning tasks such as commonsense knowledge graph completion and commonsense question answering require powerful representation learning. In this paper, we propose to learn commonsense knowledge representat…
Contrastive LearningKnowledge Graph CompletionKnowledge GraphsQuestion Answering+2Step out of KG: Knowledge Graph Completion via Knowledgeable Retrieval and Reading Comprehension
Knowledge graphs, as the cornerstone of many AI applications, usually face serious incompleteness problems. In recent years, there have been many efforts to study automatic knowledge graph completion (KGC), most of which…
Information RetrievalKnowledge Graph CompletionKnowledge GraphsReading Comprehension+1Relational 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 CompletionRelationLambdaKG: A Library for Pre-trained Language Model-Based Knowledge Graph Embeddings
Knowledge Graphs (KGs) often have two characteristics: heterogeneous graph structure and text-rich entity/relation information. Text-based KG embeddings can represent entities by encoding descriptions with pre-trained la…
Graph Representation LearningKnowledge Graph CompletionKnowledge Graph EmbeddingsKnowledge Graphs+5Learning Hierarchy-Aware Quaternion Knowledge Graph Embeddings with Representing Relations as 3D Rotations
Knowledge graph embedding aims to represent entities and relations as low-dimensional vectors, which is an effective way for predicting missing links. It is crucial for knowledge graph embedding models to model and infer…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingKnowledge Graph Embeddings+1Decoupling Mixture-of-Graphs: Unseen Relational Learning for Knowledge Graph Completion by Fusing Ontology and Textual Experts
Knowledge Graph Embedding (KGE) has been proposed and successfully utilized to knowledge Graph Completion (KGC). But classic KGE paradigm often fail in unseen relation representations. Previous studies mainly utilize the…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingRelation+1Data Augmentation for Few-Shot Knowledge Graph Completion from Hierarchical Perspective
Few-shot knowledge graph completion (FKGC) has become a new research focus in the field of knowledge graphs in recent years, which aims to predict the missing links for relations that only have a few associative triples.…
Data AugmentationKnowledge Graph CompletionKnowledge GraphsTemporal Knowledge Graph Completion with Approximated Gaussian Process Embedding
Knowledge Graphs (KGs) stores world knowledge that benefits various reasoning-based applications. Due to their incompleteness, a fundamental task for KGs, which is known as Knowledge Graph Completion (KGC), is to perform…
Gaussian ProcessesKnowledge Graph CompletionKnowledge GraphsLink Prediction+2Rethinking Knowledge Graph Evaluation Under the Open-World Assumption
Most knowledge graphs (KGs) are incomplete, which motivates one important research topic on automatically complementing knowledge graphs. However, evaluation of knowledge graph completion (KGC) models often ignores the i…
FairnessKnowledge Graph CompletionKnowledge GraphsJoint Language Semantic and Structure Embedding for Knowledge Graph Completion
The task of completing knowledge triplets has broad downstream applications. Both structural and semantic information plays an important role in knowledge graph completion. Unlike previous approaches that rely on either …
Knowledge Graph CompletionKnowledge GraphsLink Prediction