Papers Knowledge Graph Completion
“Knowledge Graph Completion” 태그가 달린 논문 541편 · 필터 해제
Tackling Long-Tailed Relations and Uncommon Entities in Knowledge Graph Completion
For large-scale knowledge graphs (KGs), recent research has been focusing on the large proportion of infrequent relations which have been ignored by previous studies. For example few-shot learning paradigm for relations …
Few-Shot LearningKnowledge Graph CompletionKnowledge GraphsMeta-LearningNoiGAN: NOISE AWARE KNOWLEDGE GRAPH EMBEDDING WITH GAN
Knowledge graph has gained increasing attention in recent years for its successful applications of numerous tasks. Despite the rapid growth of knowledge construction, knowledge graphs still suffer from severe incompletio…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingKnowledge GraphsKnowledge Graph Embedding: A Probabilistic Perspective and Generalization Bounds
We study theoretical properties of embedding methods for knowledge graph completion under the missing completely at random assumption. We prove generalization error bounds for this setting. Even though the missing comple…
Generalization BoundsGraph EmbeddingKnowledge Graph CompletionKnowledge Graph Embedding+1Extracting Conceptual Knowledge from Natural Language Text Using Maximum Likelihood Principle
Domain-specific knowledge graphs constructed from natural language text are ubiquitous in today's world. In many such scenarios the base text, from which the knowledge graph is constructed, concerns itself with practical…
Knowledge Graph CompletionKnowledge GraphsExploring Scholarly Data by Semantic Query on Knowledge Graph Embedding Space
The trends of open science have enabled several open scholarly datasets which include millions of papers and authors. Managing, exploring, and utilizing such large and complicated datasets effectively are challenging. In…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingKnowledge Graphs+3Combination of Unified Embedding Model and Observed Features for Knowledge Graph Completion
Knowledge graphs are useful for many artificial intelligence tasks but often have missing data. Hence, a method for completing knowledge graphs is required. Existing approaches include embedding models, the Path Ranking …
Knowledge Graph CompletionKnowledge GraphsLink PredictionPrediction+1Composing Knowledge Graph Embeddings via Word Embeddings
Learning knowledge graph embedding from an existing knowledge graph is very important to knowledge graph completion. For a fact $(h,r,t)$ with the head entity $h$ having a relation $r$ with the tail entity $t$, the curre…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingKnowledge Graph Embeddings+2KG-BERT: BERT for Knowledge Graph Completion
Knowledge graphs are important resources for many artificial intelligence tasks but often suffer from incompleteness. In this work, we propose to use pre-trained language models for knowledge graph completion. We treat t…
Knowledge Graph CompletionKnowledge GraphsLanguage ModelingLanguage Modelling+5Toward Understanding The Effect Of Loss function On Then Performance Of Knowledge Graph Embedding
Knowledge graphs (KGs) represent world's facts in structured forms. KG completion exploits the existing facts in a KG to discover new ones. Translation-based embedding model (TransE) is a prominent formulation to do KG c…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingKnowledge Graphs+1Diachronic Embedding for Temporal Knowledge Graph Completion
Knowledge graphs (KGs) typically contain temporal facts indicating relationships among entities at different times. Due to their incompleteness, several approaches have been proposed to infer new facts for a KG based on …
Knowledge Graph CompletionKnowledge GraphsTemporal Knowledge Graph CompletionA2N: Attending to Neighbors for Knowledge Graph Inference
State-of-the-art models for knowledge graph completion aim at learning a fixed embedding representation of entities in a multi-relational graph which can generalize to infer unseen entity relationships at test time. This…
Knowledge Graph CompletionLink PredictionExtracting Novel Facts from Tables for Knowledge Graph Completion (Extended version)
We propose a new end-to-end method for extending a Knowledge Graph (KG) from tables. Existing techniques tend to interpret tables by focusing on information that is already in the KG, and therefore tend to extract many r…
Knowledge Graph CompletionAn Open-World Extension to Knowledge Graph Completion Models
We present a novel extension to embedding-based knowledge graph completion models which enables them to perform open-world link prediction, i.e. to predict facts for entities unseen in training based on their textual des…
Knowledge Graph CompletionLink PredictionPredictionWord EmbeddingsLearning Attention-based Embeddings for Relation Prediction in Knowledge Graphs
The recent proliferation of knowledge graphs (KGs) coupled with incomplete or partial information, in the form of missing relations (links) between entities, has fueled a lot of research on knowledge base completion (als…
Knowledge Base CompletionKnowledge Graph CompletionKnowledge Graph EmbeddingsKnowledge Graphs+3Automated assessment of knowledge hierarchy evolution: comparing directed acyclic graphs
Automated construction of knowledge hierarchies from huge data corpora is gaining increasing attention in recent years, in order to tackle the infeasibility of manually extracting and semantically linking millions of con…
Knowledge Graph CompletionKnowledge GraphsQuestion AnsweringKnowledge Hypergraphs: Prediction Beyond Binary Relations
Knowledge graphs store facts using relations between two entities. In this work, we address the question of link prediction in knowledge hypergraphs where relations are defined on any number of entities. While techniques…
Entity EmbeddingsKnowledge Graph CompletionKnowledge GraphsLink Prediction+2Abstract Graphs and Abstract Paths for Knowledge Graph Completion
Knowledge graphs, which provide numerous facts in a machine-friendly format, are incomplete. Information that we induce from such graphs {--} e.g. entity embeddings, relation representations or patterns {--} will be affe…
Entity EmbeddingsKnowledge Graph CompletionKnowledge GraphsLink PredictionDistantly Supervised Biomedical Knowledge Acquisition via Knowledge Graph Based Attention
The increased demand for structured scientific knowledge has attracted considerable attention in extracting scientific relation from the ever growing scientific publications. Distant supervision is widely applied approac…
Knowledge Graph CompletionRelationRelation ExtractionTuckER: Tensor Factorization for Knowledge Graph Completion
Knowledge graphs are structured representations of real world facts. However, they typically contain only a small subset of all possible facts. Link prediction is the task of inferring missing facts based on existing one…
Knowledge Graph CompletionKnowledge GraphsLink PredictionMulti-Task Learning+1Soft Marginal TransE for Scholarly Knowledge Graph Completion
Knowledge graphs (KGs), i.e. representation of information as a semantic graph, provide a significant test bed for many tasks including question answering, recommendation, and link prediction. Various amount of scholarly…
Knowledge Graph CompletionKnowledge GraphsLink PredictionQuestion Answering