Resilience in Knowledge Graph Embeddings

TL;DR

This paper provides a comprehensive survey on resilience in knowledge graph embeddings, addressing various challenges like noise, missing data, and adversarial attacks, and highlights the need for broader resilience research beyond robustness.

Contribution

It offers a unified definition of resilience in knowledge graph embeddings and systematically reviews existing work, identifying gaps and future research directions.

Findings

Most existing work focuses on robustness of KGE models.

Resilience encompasses generalization, consistency, adaptation, and robustness.

Survey highlights the need for broader resilience research in KGE.

Abstract

In recent years, knowledge graphs have gained interest and witnessed widespread applications in various domains, such as information retrieval, question-answering, recommendation systems, amongst others. Large-scale knowledge graphs to this end have demonstrated their utility in effectively representing structured knowledge. To further facilitate the application of machine learning techniques, knowledge graph embedding (KGE) models have been developed. Such models can transform entities and relationships within knowledge graphs into vectors. However, these embedding models often face challenges related to noise, missing information, distribution shift, adversarial attacks, etc. This can lead to sub-optimal embeddings and incorrect inferences, thereby negatively impacting downstream applications. While the existing literature has focused so far on adversarial attacks on KGE models, the challenges related to the other critical aspects remain unexplored. In this paper, we, first of all, give a unified definition of resilience, encompassing several factors such as generalisation, performance consistency, distribution adaption, and robustness. After formalizing these concepts for machine learning in general, we define them in the context of knowledge graphs. To find the gap in the existing works on resilience in the context of knowledge graphs, we perform a systematic survey, taking into account all these aspects mentioned previously. Our survey results show that most of the existing works focus on a specific aspect of resilience, namely robustness. After categorizing such works based on their respective aspects of resilience, we discuss the challenges and future research directions.