A Survey on Knowledge Graph Embedding: Approaches, Applications and Benchmarks

Abstract

A knowledge graph (KG), also known as a knowledge base, is a particular kind of network structure in which the node indicates entity and the edge represent relation. However, with the explosion of network volume, the problem of data sparsity that causes large-scale KG systems to calculate and manage difficultly has become more significant. For alleviating the issue, knowledge graph embedding is proposed to embed entities and relations in a KG to a low-, dense and continuous feature space, and endow the yield model with abilities of knowledge inference and fusion. In recent years, many researchers have poured much attention in this approach, and we will systematically introduce the existing state-of-the-art approaches and a variety of applications that benefit from these methods in this paper. In addition, we discuss future prospects for the development of techniques and application trends. Specifically, we first introduce the embedding models that only leverage the information of observed triplets in the KG. We illustrate the overall framework and specific idea and compare the advantages and disadvantages of such approaches. Next, we introduce the advanced models that utilize additional semantic information to improve the performance of the original methods. We divide the additional information into two categories, including textual descriptions and relation paths. The extension approaches in each category are described, following the same classification criteria as those defined for the triplet fact-based models. We then describe two experiments for comparing the performance of listed methods and mention some broader domain tasks such as question answering, recommender systems, and so forth. Finally, we collect several hurdles that need to be overcome and provide a few future research directions for knowledge graph embedding.

A Comprehensive Survey of Knowledge Graph-Based Recommender Systems: Technologies, Development, and Contributions

“…In recommendation, KGE can be used to enrich the information of users and items, then the embedding representations can be used to calculate the similarity between both [6]. Among the most popular KGE models are TransE, TransH, TransD, and TransR [65].…”

Representation Learning for Fine-Grained Change Detection

“…Furthermore, relations can be represented as operations in the vector space, e.g., vectors, matrices, tensors, multivariate Gaussian distributions or even mixtures of Gaussians. The embedding process involves a scoring function to measure the plausibility of each relation on which optimisation is performed to maximise the total plausibility of the graph and mitigate data sparsity [ 42 ].…”

Knowledge Graph Representation Learning With Multi-Scale Capsule-Based Embedding Model Incorporating Entity Descriptions

“…The calculation process for the synthetic representation of the tail entity v t is in a same way. The calculations for the head and tail entity are shown in (13) and (14).…”

“…An example triple from Freebase looks like this: (Hamlet, story_by, William_Shakespeare). Although effective in representing structured data, two chal-lenges arise when manipulating KGs, the computational complexity problem and the data sparsity problem [14].…”