Papers Knowledge Graph Completion
“Knowledge Graph Completion” 태그가 달린 논문 541편 · 필터 해제
CAKE: A Scalable Commonsense-Aware Framework For Multi-View Knowledge Graph Completion
Knowledge graphs store a large number of factual triples while they are still incomplete, inevitably. The previous knowledge graph completion (KGC) models predict missing links between entities merely relying on fact-vie…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingKnowledge Graphs+1Geometric Algebra based Embeddings for Static and Temporal Knowledge Graph Completion
Recent years, Knowledge Graph Embeddings (KGEs) have shown promising performance on link prediction tasks by mapping the entities and relations from a Knowledge Graph (KG) into a geometric space and thus have gained incr…
Knowledge Graph CompletionKnowledge Graph EmbeddingsKnowledge GraphsLink Prediction+3Mining On Alzheimer's Diseases Related Knowledge Graph to Identity Potential AD-related Semantic Triples for Drug Repurposing
To date, there are no effective treatments for most neurodegenerative diseases. Knowledge graphs can provide comprehensive and semantic representation for heterogeneous data, and have been successfully leveraged in many …
Graph MiningKnowledge Graph CompletionKnowledge GraphsDiscovering Fine-Grained Semantics in Knowledge Graph Relations
When it comes to comprehending and analyzing multi-relational data, the semantics of relations are crucial. Polysemous relations between different types of entities, that represent multiple semantics, are common in real-…
Knowledge Graph CompletionKnowledge GraphsQuestion AnsweringRethinking Graph Convolutional Networks in Knowledge Graph Completion
Graph convolutional networks (GCNs) -- which are effective in modeling graph structures -- have been increasingly popular in knowledge graph completion (KGC). GCN-based KGC models first use GCNs to generate expressive en…
Entity EmbeddingsGraph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingTowards Loosely-Coupling Knowledge Graph Embeddings and Ontology-based Reasoning
Knowledge graph completion (a.k.a.~link prediction), i.e.,~the task of inferring missing information from knowledge graphs, is a widely used task in many applications, such as product recommendation and question answerin…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingKnowledge Graph Embeddings+5From Discrimination to Generation: Knowledge Graph Completion with Generative Transformer
Knowledge graph completion aims to address the problem of extending a KG with missing triples. In this paper, we provide an approach GenKGC, which converts knowledge graph completion to sequence-to-sequence generation ta…
Knowledge Graph CompletionLanguage ModelingLanguage ModellingLink Prediction+1Ontology-enhanced Prompt-tuning for Few-shot Learning
Few-shot Learning (FSL) is aimed to make predictions based on a limited number of samples. Structured data such as knowledge graphs and ontology libraries has been leveraged to benefit the few-shot setting in various tas…
Event ExtractionFew-Shot LearningKnowledge Graph CompletionKnowledge Graphs+1Trustworthy Knowledge Graph Completion Based on Multi-sourced Noisy Data
Knowledge graphs (KGs) have become a valuable asset for many AI applications. Although some KGs contain plenty of facts, they are widely acknowledged as incomplete. To address this issue, many KG completion methods are p…
Graph Neural NetworkKnowledge Graph CompletionKnowledge GraphsTemporal Knowledge Graph Completion: A Survey
Knowledge graph completion (KGC) can predict missing links and is crucial for real-world knowledge graphs, which widely suffer from incompleteness. KGC methods assume a knowledge graph is static, but that may lead to ina…
Knowledge Graph CompletionKnowledge GraphsSurveyTemporal Knowledge Graph Completion+1A Simple But Powerful Graph Encoder for Temporal Knowledge Graph Completion
While knowledge graphs contain rich semantic knowledge about various entities and the relational information among them, temporal knowledge graphs (TKGs) describe and model the interactions of the entities over time. In …
Knowledge Graph CompletionKnowledge GraphsTemporal Knowledge Graph CompletionLearning to Borrow– Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph Completion
Prior work on integrating text corpora with knowledge graphs (KGs) to improve Knowledge Graph Embedding (KGE) have obtained good performance for entities that co-occur in sentences in text corpora. Such sentences (textua…
Entity EmbeddingsGraph EmbeddingKnowledge Graph CompletionKnowledge Graph Embedding+4Multi-task Pre-training Language Model for Semantic Network Completion
Semantic networks, such as the knowledge graph, can represent the knowledge leveraging the graph structure. Although the knowledge graph shows promising values in natural language processing, it suffers from incompletene…
Contrastive LearningData AugmentationKnowledge Graph CompletionKnowledge Graph Embedding+4Self-attention Presents Low-dimensional Knowledge Graph Embeddings for Link Prediction
A few models have tried to tackle the link prediction problem, also known as knowledge graph completion, by embedding knowledge graphs in comparably lower dimensions. However, the state-of-the-art results are attained at…
Knowledge Graph CompletionKnowledge Graph EmbeddingKnowledge Graph EmbeddingsKnowledge Graphs+1Incomplete Knowledge Graph Alignment
Knowledge graph (KG) alignment - the task of recognizing entities referring to the same thing in different KGs - is recognized as one of the most important operations in the field of KG construction and completion. Howev…
Graph Neural NetworkKnowledge Graph CompletionKnowledge GraphsRepresentation LearningTwo-view Graph Neural Networks for Knowledge Graph Completion
We present an effective graph neural network (GNN)-based knowledge graph embedding model, which we name WGE, to capture entity- and relation-focused graph structures. Given a knowledge graph, WGE builds a single undirect…
Graph EmbeddingGraph Neural NetworkKnowledge Graph CompletionKnowledge Graph Embedding+2A Simple But Powerful Graph Encoder for Temporal Knowledge Graph Completion
Knowledge graphs contain rich knowledge about various entities and the relational information among them, while temporal knowledge graphs (TKGs) describe and model the interactions of the entities over time. In this cont…
Knowledge Graph CompletionKnowledge GraphsTemporal Knowledge Graph CompletionINDIGO: GNN-Based Inductive Knowledge Graph Completion Using Pair-Wise Encoding
The aim of knowledge graph (KG) completion is to extend an incomplete KG with missing triples. Popular approaches based on graph embeddings typically work by first representing the KG in a vector space, and then applying…
Inductive knowledge graph completionKnowledge Graph CompletionCross-lingual Alignment of Knowledge Graph Triples with Sentences
The pairing of natural language sentences with knowledge graph triples is essential for many downstream tasks like data-to-text generation, facts extraction from sentences (semantic parsing), knowledge graph completion, …
Data-to-Text GenerationKnowledge Graph CompletionNERSemantic Parsing+4Triple Classification for Scholarly Knowledge Graph Completion
Scholarly Knowledge Graphs (KGs) provide a rich source of structured information representing knowledge encoded in scientific publications. With the sheer volume of published scientific literature comprising a plethora o…
ClassificationKnowledge Graph CompletionKnowledge GraphsLink Prediction+2