A Survey of Knowledge Graph Embedding and Their Applications
This is an incremental survey paper that synthesizes existing research for practitioners and researchers in knowledge representation and applications.
This survey paper examines the development of knowledge graph embedding techniques from simple translation-based models to enrichment-based models that incorporate text, image, and context information, highlighting their utility in real-world applications like knowledge graph completion, recommender systems, and question answering.
Knowledge Graph embedding provides a versatile technique for representing knowledge. These techniques can be used in a variety of applications such as completion of knowledge graph to predict missing information, recommender systems, question answering, query expansion, etc. The information embedded in Knowledge graph though being structured is challenging to consume in a real-world application. Knowledge graph embedding enables the real-world application to consume information to improve performance. Knowledge graph embedding is an active research area. Most of the embedding methods focus on structure-based information. Recent research has extended the boundary to include text-based information and image-based information in entity embedding. Efforts have been made to enhance the representation with context information. This paper introduces growth in the field of KG embedding from simple translation-based models to enrichment-based models. This paper includes the utility of the Knowledge graph in real-world applications.