Knowledge Graph Completion using Structural and Textual Embeddings

arXiv:2404.16206v1 Announce Type: cross
Abstract: Knowledge Graphs (KGs) are widely employed in artificial intelligence applications, such as question-answering and recommendation systems. However, KGs are frequently found to be incomplete. While much of the existing literature focuses on predicting missing nodes for given incomplete KG triples, there remains an opportunity to complete KGs by exploring relations between existing nodes, a task known as relation prediction. In this study, we propose a relations prediction model that harnesses both textual and structural information …

abstract applications artificial artificial intelligence arxiv cs.ai cs.cl embeddings found graph graphs however intelligence knowledge knowledge graph knowledge graphs literature nodes question recommendation recommendation systems systems textual type while