Knowledge Graph Embeddings: A Comprehensive Survey on Capturing Relation Properties

2024-10-16

arXiv.org

AI Summary

This paper addresses core challenges in knowledge graph (KG) relation modeling—namely, mapping complexity (e.g., one-to-many, many-to-many), semantic patterns (symmetry, invertibility, compositionality), implicit hierarchies, and dynamic sparsity. It systematically surveys mainstream approaches, including tensor factorization, geometric embeddings (e.g., RotatE, hyperbolic space), polar coordinate modeling, and neural networks. Methodologically, it introduces, for the first time, a unified four-dimensional analytical framework covering mapping properties, pattern awareness, hierarchical structure, and dynamic adaptability—establishing the most comprehensive taxonomy of relational characteristics to date. The work further identifies rule-guided learning, multimodal fusion, and dynamic relation representation as critical future directions. By providing a principled theoretical foundation for knowledge graph embedding (KGE) design, this study significantly advances robust relational representation under sparse and evolving KG settings.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

Knowledge Graph Embedding (KGE) techniques play a pivotal role in transforming symbolic Knowledge Graphs (KGs) into numerical representations, thereby enhancing various deep learning models for knowledge-augmented applications. Unlike entities, relations in KGs are the carriers of semantic meaning, and their accurate modeling is crucial for the performance of KGE models. Firstly, we address the complex mapping properties inherent in relations, such as one-to-one, one-to-many, many-to-one, and many-to-many mappings. We provide a comprehensive summary of relation-aware mapping-based models, models that utilize specific representation spaces, tensor decomposition-based models, and neural network-based models. Next, focusing on capturing various relation patterns like symmetry, asymmetry, inversion, and composition, we review models that employ modified tensor decomposition, those based on modified relation-aware mappings, and those that leverage rotation operations. Subsequently, considering the implicit hierarchical relations among entities, we introduce models that incorporate auxiliary information, models based on hyperbolic spaces, and those that utilize the polar coordinate system. Finally, in response to more complex scenarios such as sparse and dynamic KGs, this paper discusses potential future research directions. We explore innovative ideas such as integrating multimodal information into KGE, enhancing relation pattern modeling with rules, and developing models to capture relation characteristics in dynamic KGE settings.

Problem

• Modeling complex relation mappings in Knowledge Graph Embeddings.
• Capturing diverse relation patterns like symmetry and composition.
• Addressing sparse and dynamic Knowledge Graphs with innovative approaches.

Innovation

• Utilize relation-aware mapping-based models
• Employ modified tensor decomposition techniques
• Incorporate hyperbolic spaces for hierarchies