Papers Knowledge Graph Completion
“Knowledge Graph Completion” 태그가 달린 논문 541편 · 필터 해제
Knowledge Graph Completion using Structural and Textual Embeddings
Knowledge Graphs (KGs) are widely employed in artificial intelligence applications, such as question-answering and recommendation systems. However, KGs are frequently found to be incomplete. While much of the existing li…
Knowledge Graph CompletionKnowledge GraphsLanguage ModelingLanguage Modelling+5One 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 CompletionCausalLP: Learning causal relations with weighted knowledge graph link prediction
Causal networks are useful in a wide variety of applications, from medical diagnosis to root-cause analysis in manufacturing. In practice, however, causal networks are often incomplete with missing causal relations. This…
Causal DiscoveryGraph EmbeddingKnowledge Graph CompletionKnowledge Graph Embedding+4Tokenization, Fusion, and Augmentation: Towards Fine-grained Multi-modal Entity Representation
Multi-modal knowledge graph completion (MMKGC) aims to discover unobserved knowledge from given knowledge graphs, collaboratively leveraging structural information from the triples and multi-modal information of the enti…
Contrastive LearningDescriptiveKnowledge Graph CompletionKnowledge Graphs+3HyperMono: A Monotonicity-aware Approach to Hyper-Relational Knowledge Representation
In a hyper-relational knowledge graph (HKG), each fact is composed of a main triple associated with attribute-value qualifiers, which express additional factual knowledge. The hyper-relational knowledge graph completion …
AttributeKnowledge Graph CompletionProgressive Knowledge Graph Completion
Knowledge Graph Completion (KGC) has emerged as a promising solution to address the issue of incompleteness within Knowledge Graphs (KGs). Traditional KGC research primarily centers on triple classification and link pred…
Knowledge Graph CompletionKnowledge GraphsLink PredictionTriple ClassificationA Foundation Model for Zero-shot Logical Query Reasoning
Complex logical query answering (CLQA) in knowledge graphs (KGs) goes beyond simple KG completion and aims at answering compositional queries comprised of multiple projections and logical operations. Existing CLQA method…
Complex Query AnsweringKnowledge Graph CompletionKnowledge GraphsZero-Shot Relational Learning for Multimodal Knowledge Graphs
Relational learning is an essential task in the domain of knowledge representation, particularly in knowledge graph completion (KGC). While relational learning in traditional single-modal settings has been extensively st…
Knowledge Graph CompletionKnowledge GraphsRelational ReasoningKGExplainer: Towards Exploring Connected Subgraph Explanations for Knowledge Graph Completion
Knowledge graph completion (KGC) aims to alleviate the inherent incompleteness of knowledge graphs (KGs), which is a critical task for various applications, such as recommendations on the web. Although knowledge graph em…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingKnowledge GraphsHarnessing the Power of Large Language Model for Uncertainty Aware Graph Processing
Handling graph data is one of the most difficult tasks. Traditional techniques, such as those based on geometry and matrix factorization, rely on assumptions about the data relations that become inadequate when handling …
Graph ClassificationKnowledge Graph CompletionLanguage ModelingLanguage Modelling+2IME: Integrating Multi-curvature Shared and Specific Embedding for Temporal Knowledge Graph Completion
Temporal Knowledge Graphs (TKGs) incorporate a temporal dimension, allowing for a precise capture of the evolution of knowledge and reflecting the dynamic nature of the real world. Typically, TKGs contain complex geometr…
Knowledge Graph CompletionKnowledge GraphsTemporal Knowledge Graph CompletionKC-GenRe: A Knowledge-constrained Generative Re-ranking Method Based on Large Language Models for Knowledge Graph Completion
The goal of knowledge graph completion (KGC) is to predict missing facts among entities. Previous methods for KGC re-ranking are mostly built on non-generative language models to obtain the probability of each candidate.…
Knowledge Graph CompletionRe-RankingHyper-CL: Conditioning Sentence Representations with Hypernetworks
While the introduction of contrastive learning frameworks in sentence representation learning has significantly contributed to advancements in the field, it still remains unclear whether state-of-the-art sentence embeddi…
Computational EfficiencyContrastive LearningKnowledge Graph CompletionRepresentation Learning+3Counterfactual Reasoning with Knowledge Graph Embeddings
Knowledge graph embeddings (KGEs) were originally developed to infer true but missing facts in incomplete knowledge repositories. In this paper, we link knowledge graph completion and counterfactual reasoning via our new…
counterfactualCounterfactual ReasoningKnowledge Graph CompletionKnowledge Graph EmbeddingsNoise-powered Multi-modal Knowledge Graph Representation Framework
The rise of Multi-modal Pre-training highlights the necessity for a unified Multi-Modal Knowledge Graph (MMKG) representation learning framework. Such a framework is essential for embedding structured knowledge into mult…
Entity AlignmentKnowledge Graph CompletionMisconceptionsMulti-modal Entity Alignment+2HDReason: Algorithm-Hardware Codesign for Hyperdimensional Knowledge Graph Reasoning
In recent times, a plethora of hardware accelerators have been put forth for graph learning applications such as vertex classification and graph classification. However, previous works have paid little attention to Knowl…
GPUGraph ClassificationGraph LearningKnowledge Graph CompletionUncertainty-Aware Relational Graph Neural Network for Few-Shot Knowledge Graph Completion
Few-shot knowledge graph completion (FKGC) aims to query the unseen facts of a relation given its few-shot reference entity pairs. The side effect of noises due to the uncertainty of entities and triples may limit the fe…
Few-Shot LearningGraph Neural NetworkKnowledge Graph CompletionMulti-perspective Improvement of Knowledge Graph Completion with Large Language Models
Knowledge graph completion (KGC) is a widely used method to tackle incompleteness in knowledge graphs (KGs) by making predictions for missing links. Description-based KGC leverages pre-trained language models to learn en…
Knowledge Graph CompletionKnowledge GraphsLink PredictionRelation+1Temporal Knowledge Graph Completion with Time-sensitive Relations in Hypercomplex Space
Temporal knowledge graph completion (TKGC) aims to fill in missing facts within a given temporal knowledge graph at a specific time. Existing methods, operating in real or complex spaces, have demonstrated promising perf…
Knowledge Graph CompletionTemporal Knowledge Graph CompletionUnleashing the Power of Imbalanced Modality Information for Multi-modal Knowledge Graph Completion
Multi-modal knowledge graph completion (MMKGC) aims to predict the missing triples in the multi-modal knowledge graphs by incorporating structural, visual, and textual information of entities into the discriminant models…
Knowledge Graph CompletionKnowledge GraphsMulti-modal Knowledge Graph