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

Year: 2025

Type: article

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 article 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 article concludes with challenges and an outlook of future directions in the field.

Source: ACM Computing Surveys

DOI: 10.1145/3771692

Authors: Chau D. M. Nguyen, Tim French, Michael Stewart, Melinda Hodkiewicz, Wei Liu

Institution: The University of Western Australia

Language: English

Topic: Advanced Graph Neural Networks

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: Australian Government

Award: G6198541581