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Rotate3D: Representing Relations as Rotations in Three-Dimensional Space for Knowledge Graph Embedding

Published: 19 October 2020 Publication History

Abstract

Knowledge graph embedding, which aims to learn low-dimensional embeddings of entities and relations, plays a vital role in a wide range of applications. It is crucial for knowledge graph embedding models to model and infer various relation patterns, such as symmetry/antisymmetry, inversion, and composition. However, most existing methods fail to model the non-commutative composition pattern, which is essential, especially for multi-hop reasoning. To address this issue, we propose a new model called Rotate3D, which maps entities to the three-dimensional space and defines relations as rotations from head entities to tail entities. By using the non-commutative composition property of rotations in the three-dimensional space, Rotate3D can naturally preserve the order of the composition of relations. Experiments show that Rotate3D outperforms existing state-of-the-art models for link prediction and path query answering. Further case studies demonstrate that Rotate3D can effectively capture various relation patterns with a marked improvement in modeling the composition pattern.

Supplementary Material

MP4 File (3340531.3411889.mp4)
In this video, we first introduced the background of the knowledge graph embedding. Then, we pointed out that most existing knowledge graph embedding models fail to model the non-commutative composition pattern. Afterward, we described our proposed Rotate3D model in detail.

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  • (2025)Integrating Transformer Architecture and Householder Transformations for Enhanced Temporal Knowledge Graph Embedding in DuaTHPSymmetry10.3390/sym1702017317:2(173)Online publication date: 24-Jan-2025
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    cover image ACM Conferences
    CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management
    October 2020
    3619 pages
    ISBN:9781450368599
    DOI:10.1145/3340531
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    Publication History

    Published: 19 October 2020

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    Author Tags

    1. knowledge graph embedding
    2. link prediction
    3. path query answering
    4. relation patterns

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    • Research-article

    Funding Sources

    • Shenzhen Basic Research Layout Project
    • National Key R&D Program of China
    • National Natural Science Foundation of China

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    Cited By

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    • (2025)Integrating Transformer Architecture and Householder Transformations for Enhanced Temporal Knowledge Graph Embedding in DuaTHPSymmetry10.3390/sym1702017317:2(173)Online publication date: 24-Jan-2025
    • (2025)Expressiveness Analysis and Enhancing Framework for Geometric Knowledge Graph Embedding ModelsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.348691537:1(306-318)Online publication date: Jan-2025
    • (2025)Multi-dimension rotations based on quaternion system for modeling various patterns in temporal knowledge graphsKnowledge-Based Systems10.1016/j.knosys.2025.113114311(113114)Online publication date: Feb-2025
    • (2024)Generalizing knowledge graph embedding with universal orthogonal parameterizationProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693194(28040-28059)Online publication date: 21-Jul-2024
    • (2024)Knowledge Graph Embedding Using a Multi-Channel Interactive Convolutional Neural Network with Triple AttentionMathematics10.3390/math1218282112:18(2821)Online publication date: 11-Sep-2024
    • (2024)Geometry Interaction Embeddings for Interpolation Temporal Knowledge Graph CompletionMathematics10.3390/math1213202212:13(2022)Online publication date: 28-Jun-2024
    • (2024)Knowledge Graph Embedding: A Survey from the Perspective of Representation SpacesACM Computing Surveys10.1145/3643806Online publication date: 2-Feb-2024
    • (2024)SpherE: Expressive and Interpretable Knowledge Graph Embedding for Set RetrievalProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657910(2629-2634)Online publication date: 10-Jul-2024
    • (2024)A Method for Assessing Inference Patterns Captured by Embedding Models in Knowledge GraphsProceedings of the ACM Web Conference 202410.1145/3589334.3645505(2030-2041)Online publication date: 13-May-2024
    • (2024)A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multi-ModalIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.341745146:12(9456-9478)Online publication date: Dec-2024
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