A Survey of Knowledge Graph Embedding and Their Applications

TL;DR

This survey reviews the evolution of knowledge graph embedding techniques, highlighting their applications in real-world tasks and recent advances incorporating multi-modal information and contextual enrichment.

Contribution

It provides a comprehensive overview of the progression from basic translation models to advanced enrichment-based methods in knowledge graph embedding.

Findings

Knowledge graph embedding enables effective real-world application consumption.

Recent methods incorporate text and image information into embeddings.

Embedding techniques have evolved to include contextual and multi-modal data.

Abstract

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.