Papers Knowledge Graph Completion
“Knowledge Graph Completion” 태그가 달린 논문 541편 · 필터 해제
DensE: An Enhanced Non-commutative Representation for Knowledge Graph Embedding with Adaptive Semantic Hierarchy
Capturing the composition patterns of relations is a vital task in knowledge graph completion. It also serves as a fundamental step towards multi-hop reasoning over learned knowledge. Previously, several rotation-based t…
Computational EfficiencyEntity EmbeddingsGraph EmbeddingKnowledge Graph Completion+3A Survey on Graph Neural Networks for Knowledge Graph Completion
Knowledge Graphs are increasingly becoming popular for a variety of downstream tasks like Question Answering and Information Retrieval. However, the Knowledge Graphs are often incomplete, thus leading to poor performance…
Information RetrievalKnowledge Base CompletionKnowledge Graph CompletionKnowledge Graphs+2Connecting Embeddings for Knowledge Graph Entity Typing
Knowledge graph (KG) entity typing aims at inferring possible missing entity type instances in KG, which is a very significant but still under-explored subtask of knowledge graph completion. In this paper, we propose a n…
Entity TypingKnowledge Graph CompletionVocal Bursts Type PredictionFew-shot link prediction via graph neural networks for Covid-19 drug-repurposing
Predicting interactions among heterogenous graph structured data has numerous applications such as knowledge graph completion, recommendation systems and drug discovery. Often times, the links to be predicted belong to r…
Drug DiscoveryFew-Shot LearningGraph LearningKnowledge Graph Completion+3Multi-Partition Embedding Interaction with Block Term Format for Knowledge Graph Completion
Knowledge graph completion is an important task that aims to predict the missing relational link between entities. Knowledge graph embedding methods perform this task by representing entities and relations as embedding v…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingLink Prediction+2Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction
Many practical graph problems, such as knowledge graph construction and drug-drug interaction prediction, require to handle multi-relational graphs. However, handling real-world multi-relational graphs with Graph Neural …
graph constructionKnowledge Graph CompletionLink PredictionMeta-Learning+15* Knowledge Graph Embeddings with Projective Transformations
Performing link prediction using knowledge graph embedding models has become a popular approach for knowledge graph completion. Such models employ a transformation function that maps nodes via edges into a vector space i…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingKnowledge Graph Embeddings+3A Data-driven Approach for Noise Reduction in Distantly Supervised Biomedical Relation Extraction
Fact triples are a common form of structured knowledge used within the biomedical domain. As the amount of unstructured scientific texts continues to grow, manual annotation of these texts for the task of relation extrac…
Knowledge Graph CompletionMultiple Instance LearningRelationRelation Classification+1Benchmarking neural embeddings for link prediction in knowledge graphs under semantic and structural changes
Recently, link prediction algorithms based on neural embeddings have gained tremendous popularity in the Semantic Web community, and are extensively used for knowledge graph completion. While algorithmic advances have st…
BenchmarkingKnowledge Graph CompletionKnowledge GraphsLink PredictionKnowledge Base Completion: Baseline strikes back (Again)
Knowledge Base Completion (KBC) has been a very active area lately. Several recent KBCpapers propose architectural changes, new training methods, or even new formulations. KBC systems are usually evaluated on standard be…
AllKnowledge Base CompletionKnowledge Base PopulationKnowledge Graph Completion+3Temporal Knowledge Base Completion: New Algorithms and Evaluation Protocols
Temporal knowledge bases associate relational (s,r,o) triples with a set of times (or a single time instant) when the relation is valid. While time-agnostic KB completion (KBC) has witnessed significant research, tempora…
Knowledge Base CompletionKnowledge Graph CompletionKnowledge GraphsLink Prediction+5Structure-Augmented Text Representation Learning for Efficient Knowledge Graph Completion
Human-curated knowledge graphs provide critical supportive information to various natural language processing tasks, but these graphs are usually incomplete, urging auto-completion of them. Prevalent graph embedding appr…
Graph EmbeddingKnowledge Graph CompletionKnowledge GraphsLink Prediction+1Learning Structured Embeddings of Knowledge Graphs with Adversarial Learning Framework
Many large-scale knowledge graphs are now available and ready to provide semantically structured information that is regarded as an important resource for question answering and decision support tasks. However, they are …
General ClassificationKnowledge Graph CompletionKnowledge GraphsLink Prediction+3Exploring Effects of Random Walk Based Minibatch Selection Policy on Knowledge Graph Completion
In this paper, we have explored the effects of different minibatch sampling techniques in Knowledge Graph Completion. Knowledge Graph Completion (KGC) or Link Prediction is the task of predicting missing facts in a knowl…
Knowledge Graph CompletionLink PredictionReinforced Anytime Bottom Up Rule Learning for Knowledge Graph Completion
Most of todays work on knowledge graph completion is concerned with sub-symbolic approaches that focus on the concept of embedding a given graph in a low dimensional vector space. Against this trend, we propose an approa…
Knowledge Graph Completionreinforcement-learningReinforcement LearningReinforcement Learning (RL)Evaluating the Calibration of Knowledge Graph Embeddings for Trustworthy Link Prediction
Little is known about the trustworthiness of predictions made by knowledge graph embedding (KGE) models. In this paper we take initial steps toward this direction by investigating the calibration of KGE models, or the ex…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingKnowledge Graph Embeddings+2Mining Implicit Entity Preference from User-Item Interaction Data for Knowledge Graph Completion via Adversarial Learning
The task of Knowledge Graph Completion (KGC) aims to automatically infer the missing fact information in Knowledge Graph (KG). In this paper, we take a new perspective that aims to leverage rich user-item interaction dat…
Knowledge Graph CompletionRealistic Re-evaluation of Knowledge Graph Completion Methods: An Experimental Study
In the active research area of employing embedding models for knowledge graph completion, particularly for the task of link prediction, most prior studies used two benchmark datasets FB15k and WN18 in evaluating such mod…
Knowledge Graph CompletionLink PredictionPredictionTowards Learning Instantiated Logical Rules from Knowledge Graphs
Efficiently inducing high-level interpretable regularities from knowledge graphs (KGs) is an essential yet challenging task that benefits many downstream applications. In this work, we present GPFL, a probabilistic rule …
Inductive knowledge graph completionKnowledge Graph CompletionKnowledge GraphsIs Aligning Embedding Spaces a Challenging Task? A Study on Heterogeneous Embedding Alignment Methods
Representation Learning of words and Knowledge Graphs (KG) into low dimensional vector spaces along with its applications to many real-world scenarios have recently gained momentum. In order to make use of multiple KG em…
Entity DisambiguationKnowledge Graph CompletionKnowledge GraphsQuestion Answering+1