A Survey on Recent Random Walk-based Methods for Embedding Knowledge Graphs

TL;DR

This paper reviews recent random walk-based embedding methods for knowledge graphs, highlighting their importance in transforming high-dimensional data into meaningful low-dimensional representations for various machine learning applications.

Contribution

It provides a comprehensive survey of recent random walk-based techniques for knowledge graph embedding, emphasizing their methodologies and applications.

Findings

Summarizes key random walk-based embedding methods

Highlights advantages of these methods in knowledge graph tasks

Identifies open challenges and future directions

Abstract

Machine learning, deep learning, and NLP methods on knowledge graphs are present in different fields and have important roles in various domains from self-driving cars to friend recommendations on social media platforms. However, to apply these methods to knowledge graphs, the data usually needs to be in an acceptable size and format. In fact, knowledge graphs normally have high dimensions and therefore we need to transform them to a low-dimensional vector space. An embedding is a low-dimensional space into which you can translate high dimensional vectors in a way that intrinsic features of the input data are preserved. In this review, we first explain knowledge graphs and their embedding and then review some of the random walk-based embedding methods that have been developed recently.