Papers Knowledge Graph Embedding
“Knowledge Graph Embedding” 태그가 달린 논문 499편 · 필터 해제
From Latent to Lucid: Transforming Knowledge Graph Embeddings into Interpretable Structures with KGEPrisma
In this paper, we introduce a post-hoc and local explainable AI method tailored for Knowledge Graph Embedding (KGE) models. These models are essential to Knowledge Graph Completion yet criticized for their opaque, black-…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingKnowledge Graph Embeddings+1LinkLogic: A New Method and Benchmark for Explainable Knowledge Graph Predictions
While there are a plethora of methods for link prediction in knowledge graphs, state-of-the-art approaches are often black box, obfuscating model reasoning and thereby limiting the ability of users to make informed decis…
Graph EmbeddingKnowledge Graph EmbeddingKnowledge GraphsLink Prediction+1KG-FIT: Knowledge Graph Fine-Tuning Upon Open-World Knowledge
Knowledge Graph Embedding (KGE) techniques are crucial in learning compact representations of entities and relations within a knowledge graph, facilitating efficient reasoning and knowledge discovery. While existing meth…
Graph EmbeddingInformativenessKnowledge Graph EmbeddingLanguage Modeling+3Harmonizing Human Insights and AI Precision: Hand in Hand for Advancing Knowledge Graph Task
Knowledge graph embedding (KGE) has caught significant interest for its effectiveness in knowledge graph completion (KGC), specifically link prediction (LP), with recent KGE models cracking the LP benchmarks. Despite the…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingLink PredictionGeneralizing Knowledge Graph Embedding with Universal Orthogonal Parameterization
Recent advances in knowledge graph embedding (KGE) rely on Euclidean/hyperbolic orthogonal relation transformations to model intrinsic logical patterns and topological structures. However, existing approaches are confine…
Graph EmbeddingKnowledge Graph EmbeddingKnowledge GraphsUntargeted Adversarial Attack on Knowledge Graph Embeddings
Knowledge graph embedding (KGE) methods have achieved great success in handling various knowledge graph (KG) downstream tasks. However, KGE methods may learn biased representations on low-quality KGs that are prevalent i…
Adversarial AttackGraph EmbeddingKnowledge Graph EmbeddingKnowledge Graph Embeddings+1Towards Continual Knowledge Graph Embedding via Incremental Distillation
Traditional knowledge graph embedding (KGE) methods typically require preserving the entire knowledge graph (KG) with significant training costs when new knowledge emerges. To address this issue, the continual knowledge …
Graph EmbeddingKnowledge Graph EmbeddingArbitrary Time Information Modeling via Polynomial Approximation for Temporal Knowledge Graph Embedding
Distinguished from traditional knowledge graphs (KGs), temporal knowledge graphs (TKGs) must explore and reason over temporally evolving facts adequately. However, existing TKG approaches still face two main challenges, …
Graph EmbeddingKnowledge Graph EmbeddingKnowledge GraphsSpherE: Expressive and Interpretable Knowledge Graph Embedding for Set Retrieval
Knowledge graphs (KGs), which store an extensive number of relational facts (head, relation, tail), serve various applications. While many downstream tasks highly rely on the expressive modeling and predictive embedding …
Graph EmbeddingKnowledge Graph EmbeddingKnowledge GraphsRepresentation Learning+1CausalLP: 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+4Sharing Parameter by Conjugation for Knowledge Graph Embeddings in Complex Space
A Knowledge Graph (KG) is the directed graphical representation of entities and relations in the real world. KG can be applied in diverse Natural Language Processing (NLP) tasks where knowledge is required. The need to s…
Graph EmbeddingKnowledge Graph EmbeddingKnowledge Graph EmbeddingsSurvey on Embedding Models for Knowledge Graph and its Applications
Knowledge Graph (KG) is a graph based data structure to represent facts of the world where nodes represent real world entities or abstract concept and edges represent relation between the entities. Graph as representatio…
Feature EngineeringGraph EmbeddingKnowledge Graph EmbeddingRelation+1Mitigating Heterogeneity among Factor Tensors via Lie Group Manifolds for Tensor Decomposition Based Temporal Knowledge Graph Embedding
Recent studies have highlighted the effectiveness of tensor decomposition methods in the Temporal Knowledge Graphs Embedding (TKGE) task. However, we found that inherent heterogeneity among factor tensors in tensor decom…
Graph EmbeddingKnowledge Graph EmbeddingKnowledge GraphsLink Prediction+2KGExplainer: 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 GraphsOpen Knowledge Base Canonicalization with Multi-task Learning
The construction of large open knowledge bases (OKBs) is integral to many knowledge-driven applications on the world wide web such as web search. However, noun phrases and relational phrases in OKBs often suffer from red…
ClusteringGraph EmbeddingKnowledge Graph EmbeddingMulti-Task LearningStructural Positional Encoding for knowledge integration in transformer-based medical process monitoring
Predictive process monitoring is a process mining task aimed at forecasting information about a running process trace, such as the most correct next activity to be executed. In medical domains, predictive process monitor…
Knowledge Graph EmbeddingManagementOntology EmbeddingPredictive Process Monitoring+1Negative Sampling in Knowledge Graph Representation Learning: A Review
Knowledge Graph Representation Learning (KGRL), or Knowledge Graph Embedding (KGE), is essential for AI applications such as knowledge construction and information retrieval. These models encode entities and relations in…
Graph EmbeddingGraph Representation LearningInformation RetrievalKnowledge Graph Embedding+5EntailE: Introducing Textual Entailment in Commonsense Knowledge Graph Completion
Commonsense knowledge graph completion is a new challenge for commonsense knowledge graph construction and application. In contrast to factual knowledge graphs such as Freebase and YAGO, commonsense knowledge graphs (CSK…
graph constructionGraph EmbeddingKnowledge Graph CompletionKnowledge Graph Embedding+2MQuinE: a cure for "Z-paradox" in knowledge graph embedding models
Knowledge graph embedding (KGE) models achieved state-of-the-art results on many knowledge graph tasks including link prediction and information retrieval. Despite the superior performance of KGE models in practice, we d…
Graph EmbeddingInformation RetrievalKnowledge Graph EmbeddingLink Prediction+2Edge-Enabled Anomaly Detection and Information Completion for Social Network Knowledge Graphs
In the rapidly advancing information era, various human behaviors are being precisely recorded in the form of data, including identity information, criminal records, and communication data. Law enforcement agencies can e…
Anomaly DetectionEdge-computingGraph EmbeddingKnowledge Graph Completion+3