2024/02/05 · It aims to expand and enhance existing knowledge graphs by predicting missing entities, relations, and entity attributes. Especially for the ...

2024/06/25 · The relation completion for knowledge graph requires expanding and enriching a knowledge graph by predicting the missing relation in a given ...

2024/02/23 · We consider a new approach to structured systems within the transfer matrix framework. We associate with such a structured system a graph which ...

TL;DR: A data modeling approach is proposed that vectorizes the triples using two cutting-edge word embedding models, Wrod2Vec and GloVe, as well as TF-IDF ...

The relation completion for knowledge graph requires expanding and enriching a knowledge graph by predicting the missing relation in a given triple which ...

"A path-based relation networks model for knowledge graph completion". Expert Systems with Applications 2021. paper. Applied Soft Computing. (TPath) Luyi Bai ...

Embedding-based methods try to encode all relevant information into shallow embeddings, while message-passing graph neural networks (GNNs) iteratively learn the ...

To do so, the latent representation of KGs in a low dimensional vector space has been exploited to predict the missing information in order to complete the KGs.

This paper proposes a novel framework called Fact Embedding through Diffusion Model (FDM) to address the knowledge graph completion task.

Knowledge Graph Completion (KGC) aims to reason over known facts and infer missing links but achieves weak performances on those sparse Knowledge Graphs (KGs).