Papers Knowledge Graph Completion
“Knowledge Graph Completion” 태그가 달린 논문 541편 · 필터 해제
Better Together: Enhancing Generative Knowledge Graph Completion with Language Models and Neighborhood Information
Real-world Knowledge Graphs (KGs) often suffer from incompleteness, which limits their potential performance. Knowledge Graph Completion (KGC) techniques aim to address this issue. However, traditional KGC methods are co…
ImputationKnowledge Graph CompletionKnowledge GraphsWorld KnowledgeDistance-Based Propagation for Efficient Knowledge Graph Reasoning
Knowledge graph completion (KGC) aims to predict unseen edges in knowledge graphs (KGs), resulting in the discovery of new facts. A new class of methods have been proposed to tackle this problem by aggregating path infor…
Knowledge Graph CompletionKnowledge GraphsRe-Temp: Relation-Aware Temporal Representation Learning for Temporal Knowledge Graph Completion
Temporal Knowledge Graph Completion (TKGC) under the extrapolation setting aims to predict the missing entity from a fact in the future, posing a challenge that aligns more closely with real-world prediction problems. Ex…
Knowledge Graph CompletionRelationRepresentation LearningTemporal Knowledge Graph CompletionNegative Sampling with Adaptive Denoising Mixup for Knowledge Graph Embedding
Knowledge graph embedding (KGE) aims to map entities and relations of a knowledge graph (KG) into a low-dimensional and dense vector space via contrasting the positive and negative triples. In the training process of KGE…
DenoisingGraph EmbeddingKnowledge Graph CompletionKnowledge Graph Embedding+1Enhancing Text-based Knowledge Graph Completion with Zero-Shot Large Language Models: A Focus on Semantic Enhancement
The design and development of text-based knowledge graph completion (KGC) methods leveraging textual entity descriptions are at the forefront of research. These methods involve advanced optimization techniques such as so…
Contrastive LearningData AugmentationGraph EmbeddingHallucination+4Making Large Language Models Perform Better in Knowledge Graph Completion
Large language model (LLM) based knowledge graph completion (KGC) aims to predict the missing triples in the KGs with LLMs. However, research about LLM-based KGC fails to sufficiently harness LLMs' inference proficiencie…
In-Context LearningKnowledge Graph CompletionLanguage ModelingLanguage Modelling+1An Investigation of LLMs' Inefficacy in Understanding Converse Relations
Large Language Models (LLMs) have achieved remarkable success in many formal language oriented tasks, such as structural data-to-text and semantic parsing. However current benchmarks mostly follow the data distribution o…
Knowledge Graph CompletionQuestion AnsweringSemantic ParsingBenchmarking the Abilities of Large Language Models for RDF Knowledge Graph Creation and Comprehension: How Well Do LLMs Speak Turtle?
Large Language Models (LLMs) are advancing at a rapid pace, with significant improvements at natural language processing and coding tasks. Yet, their ability to work with formal languages representing data, specifically …
BenchmarkingKnowledge Graph CompletionKnowledge GraphsLeveraging Pre-trained Language Models for Time Interval Prediction in Text-Enhanced Temporal Knowledge Graphs
Most knowledge graph completion (KGC) methods learn latent representations of entities and relations of a given graph by mapping them into a vector space. Although the majority of these methods focus on static knowledge …
Knowledge Graph CompletionKnowledge GraphsLink PredictionRepresentation Learning+3KERMIT: Knowledge Graph Completion of Enhanced Relation Modeling with Inverse Transformation
Knowledge graph completion (KGC) revolves around populating missing triples in a knowledge graph using available information. Text-based methods, which depend on textual descriptions of triples, often encounter difficult…
DiversityKnowledge Graph CompletionKnowledge GraphsLink Prediction+1Prior Bilinear Based Models for Knowledge Graph Completion
Bilinear based models are powerful and widely used approaches for Knowledge Graphs Completion (KGC). Although bilinear based models have achieved significant advances, these studies mainly concentrate on posterior proper…
Knowledge Graph CompletionKnowledge GraphsModel-based Subsampling for Knowledge Graph Completion
Subsampling is effective in Knowledge Graph Embedding (KGE) for reducing overfitting caused by the sparsity in Knowledge Graph (KG) datasets. However, current subsampling approaches consider only frequencies of queries t…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingmodelExtending 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+3On the Aggregation of Rules for Knowledge Graph Completion
Rule learning approaches for knowledge graph completion are efficient, interpretable and competitive to purely neural models. The rule aggregation problem is concerned with finding one plausibility score for a candidate …
Knowledge Graph CompletionExploring Large Language Models for Knowledge Graph Completion
Knowledge graphs play a vital role in numerous artificial intelligence tasks, yet they frequently face the issue of incompleteness. In this study, we explore utilizing Large Language Models (LLM) for knowledge graph comp…
Knowledge Graph CompletionKnowledge GraphsRelationRelation Prediction+1MoCoSA: Momentum Contrast for Knowledge Graph Completion with Structure-Augmented Pre-trained Language Models
Knowledge Graph Completion (KGC) aims to conduct reasoning on the facts within knowledge graphs and automatically infer missing links. Existing methods can mainly be categorized into structure-based or description-based.…
Entity EmbeddingsKnowledge Graph CompletionKnowledge GraphsLink PredictionA Comprehensive Study on Knowledge Graph Embedding over Relational Patterns Based on Rule Learning
Knowledge Graph Embedding (KGE) has proven to be an effective approach to solving the Knowledge Graph Completion (KGC) task. Relational patterns which refer to relations with specific semantics exhibiting graph patterns …
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingSingle Particle AnalysisInductive 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 PredictionMACO: A Modality Adversarial and Contrastive Framework for Modality-missing Multi-modal Knowledge Graph Completion
Recent years have seen significant advancements in multi-modal knowledge graph completion (MMKGC). MMKGC enhances knowledge graph completion (KGC) by integrating multi-modal entity information, thereby facilitating the d…
Knowledge Graph CompletionKnowledge GraphsMulti-modal Knowledge GraphHyperFormer: Enhancing Entity and Relation Interaction for Hyper-Relational Knowledge Graph Completion
Hyper-relational knowledge graphs (HKGs) extend standard knowledge graphs by associating attribute-value qualifiers to triples, which effectively represent additional fine-grained information about its associated triple.…
AttributeKnowledge Graph CompletionKnowledge GraphsMixture-of-Experts+1