Representation Learning in Complex Logical Query Answering on Knowledge Graphs: A Survey

Abstract

Answering complex logical queries is a fundamental task in knowledge graph reasoning. Query representation learning models project queries and entities into embedding vectors in low dimensional spaces, commonly referred to as query embeddings (QE). This approach addresses the challenges of complex logical queries on incomplete large knowledge graphs and demands a comprehensive survey. This paper presents a comprehensive survey of QE methods according to query syntaxes, representation learning methods, optimization methods, datasets, evaluation metrics and model performance. We propose a taxonomy for existing QE methods and investigate issues in the representation learning of queries within and across methods. Finally, the paper concludes with challenges and an outlook of future directions in the field.