Papers Knowledge Graph Completion
“Knowledge Graph Completion” 태그가 달린 논문 541편 · 필터 해제
Knowledge Graph Completion with Text-aided Regularization
Knowledge Graph Completion is a task of expanding the knowledge graph/base through estimating possible entities, or proper nouns, that can be connected using a set of predefined relations, or verb/predicates describing i…
Knowledge Graph CompletionA shallow neural model for relation prediction
Knowledge graph completion refers to predicting missing triples. Most approaches achieve this goal by predicting entities, given an entity and a relation. We predict missing triples via the relation prediction. To this e…
Knowledge Graph CompletionmodelMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+3Quantum and Translation Embedding for Knowledge Graph Completion
Knowledge Graph Completion (KGC) mainly devotes to link predicting for an entity pair in Knowledge Graph (KG) according to known facts. In this work, we present a novel model for this end. In this model, Quantum and Tran…
Knowledge Graph CompletionTranslationT-GAP: Learning to Walk across Time for Temporal Knowledge Graph Completion
Temporal knowledge graphs (TKGs) inherently reflect the transient nature of real-world knowledge, as opposed to static knowledge graphs. Naturally, automatic TKG completion has drawn much research interests for a more re…
DecoderKnowledge Graph CompletionKnowledge GraphsRelational Reasoning+2Biomedical Knowledge Graph Refinement and Completion using Graph Representation Learning and Top-K Similarity Measure
Knowledge Graphs have been one of the fundamental methods for integrating heterogeneous data sources. Integrating heterogeneous data sources is crucial, especially in the biomedical domain, where central data-driven task…
Drug DiscoveryGraph Representation LearningKnowledge Graph CompletionKnowledge Graphs+2Learning from History: Modeling Temporal Knowledge Graphs with Sequential Copy-Generation Networks
Large knowledge graphs often grow to store temporal facts that model the dynamic relations or interactions of entities along the timeline. Since such temporal knowledge graphs often suffer from incompleteness, it is impo…
Knowledge Graph CompletionKnowledge GraphsRepresentation LearningContext-Enhanced Entity and Relation Embedding for Knowledge Graph Completion
Most researches for knowledge graph completion learn representations of entities and relations to predict missing links in incomplete knowledge graphs. However, these methods fail to take full advantage of both the conte…
Knowledge Graph CompletionKnowledge GraphsRelationBig Green at WNUT 2020 Shared Task-1: Relation Extraction as Contextualized Sequence Classification
Relation and event extraction is an important task in natural language processing. We introduce a system which uses contextualized knowledge graph completion to classify relations and events between known entities in a n…
Event ExtractionGeneral ClassificationKnowledge Graph CompletionRelation+1Multi-view Classification Model for Knowledge Graph Completion
Most previous work on knowledge graph completion conducted single-view prediction or calculation for candidate triple evaluation, based only on the content information of the candidate triples. This paper describes a nov…
ClassificationKnowledge Graph CompletionMulti-Task Learning for Knowledge Graph Completion with Pre-trained Language Models
As research on utilizing human knowledge in natural language processing has attracted considerable attention in recent years, knowledge graph (KG) completion has come into the spotlight. Recently, a new knowledge graph c…
Knowledge Graph CompletionKnowledge GraphsLanguage ModelingLanguage Modelling+3Relation Specific Transformations for Open World Knowledge Graph Completion
We propose an open-world knowledge graph completion model that can be combined with common closed-world approaches (such as ComplEx) and enhance them to exploit text-based representations for entities unseen in training.…
Knowledge Graph CompletionLink PredictionRelationWorld KnowledgeTucker decomposition-based Temporal Knowledge Graph Completion
Knowledge graphs have been demonstrated to be an effective tool for numerous intelligent applications. However, a large amount of valuable knowledge still exists implicitly in the knowledge graphs. To enrich the existing…
Knowledge Graph CompletionKnowledge GraphsLink PredictionTemporal Knowledge Graph Completion+1Association Rules Enhanced Knowledge Graph Attention Network
Most existing knowledge graphs suffer from incompleteness. Embedding knowledge graphs into continuous vector spaces has recently attracted increasing interest in knowledge base completion. However, in most existing embed…
Graph AttentionGraph EmbeddingKnowledge Base CompletionKnowledge Graph Completion+4Theoretical Rule-based Knowledge Graph Reasoning by Connectivity Dependency Discovery
Discovering precise and interpretable rules from knowledge graphs is regarded as an essential challenge, which can improve the performances of many downstream tasks and even provide new ways to approach some Natural Lang…
Knowledge Graph CompletionKnowledge GraphsLink PredictionDuality-Induced Regularizer for Tensor Factorization Based Knowledge Graph Completion
Tensor factorization based models have shown great power in knowledge graph completion (KGC). However, their performance usually suffers from the overfitting problem seriously. This motivates various regularizers -- such…
Knowledge Graph CompletionLink PredictionNode Attribute Completion in Knowledge Graphs with Multi-Relational Propagation
The existing literature on knowledge graph completion mostly focuses on the link prediction task. However, knowledge graphs have an additional incompleteness problem: their nodes possess numerical attributes, whose value…
AttributeKnowledge Graph CompletionKnowledge GraphsLink Prediction+1DyERNIE: Dynamic Evolution of Riemannian Manifold Embeddings for Temporal Knowledge Graph Completion
There has recently been increasing interest in learning representations of temporal knowledge graphs (KGs), which record the dynamic relationships between entities over time. Temporal KGs often exhibit multiple simultane…
Knowledge Graph CompletionKnowledge GraphsRepresentation LearningTemporal Knowledge Graph CompletionLearning Physical Common Sense as Knowledge Graph Completion via BERT Data Augmentation and Constrained Tucker Factorization
Physical common sense plays an essential role in the cognition abilities of robots for human-robot interaction. Machine learning methods have shown promising results on physical commonsense learning in natural language p…
Common Sense ReasoningData AugmentationGeneral KnowledgeGraph Embedding+5A Greedy Bit-flip Training Algorithm for Binarized Knowledge Graph Embeddings
This paper presents a simple and effective discrete optimization method for training binarized knowledge graph embedding model B-CP. Unlike the prior work using a SGD-based method and quantization of real-valued vectors,…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingKnowledge Graph Embeddings+1FedE: Embedding Knowledge Graphs in Federated Setting
Knowledge graphs (KGs) consisting of triples are always incomplete, so it's important to do Knowledge Graph Completion (KGC) by predicting missing triples. Multi-Source KG is a common situation in real KG applications wh…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingKnowledge Graph Embeddings+1