Survey on Embedding Models for Knowledge Graph and its Applications
This is an incremental survey paper summarizing existing methods for knowledge graph embeddings and their applications, primarily for researchers in AI and data science.
The paper surveys knowledge graph embedding models, focusing on translation-based and neural network-based approaches to address issues like data sparsity and computational complexity, and discusses their applications in domains using deep learning and social media data.
Knowledge Graph (KG) is a graph based data structure to represent facts of the world where nodes represent real world entities or abstract concept and edges represent relation between the entities. Graph as representation for knowledge has several drawbacks like data sparsity, computational complexity and manual feature engineering. Knowledge Graph embedding tackles the drawback by representing entities and relation in low dimensional vector space by capturing the semantic relation between them. There are different KG embedding models. Here, we discuss translation based and neural network based embedding models which differ based on semantic property, scoring function and architecture they use. Further, we discuss application of KG in some domains that use deep learning models and leverage social media data.