TBicomR:时态知识图谱中的事件预测与二元旋转

IF 7.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Ngoc-Trung Nguyen , Chi Tran , Thanh Le
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引用次数: 0

摘要

时态知识图谱(TKGs)捕捉了随时间演变的关系和实体,由于复杂的时间和关系动态,事件预测成为一项具有挑战性的任务。在这项工作中,我们提出了 BiCoTime,这是一种使用二元嵌入来表示实体、关系和时间的新型模型。四元数通过非交换性捕捉非对称关系,而二元数提供了交换代数结构,是对称和非对称关系建模的理想选择。与四元数不同,二复数嵌入保持了对称关系的可解释性,同时保留了关键的代数特性,如分布性。时间旋转进一步增强了 BiCoTime 对关系和时间之间的交互作用进行建模的能力,从而捕捉到实体和关系是如何演变的。这种双复嵌入和时间旋转的结合确保了对 TKGs 建模的可解释性和准确性。我们的实验表明,在强调时间点的 ICEWS14 数据集上,TBiComR 的平均互斥等级 (MRR) 提高了 21%,而在强调时间跨度的 YAGO11k 数据集上,TBiComR 的平均互斥等级 (MRR) 提高了 15%。与使用四元数或八元数的模型相比,二元数的选择平衡了计算复杂性和表现力,提供了高效的训练和更好的预测性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
TBicomR: Event Prediction in Temporal Knowledge Graphs with Bicomplex Rotation
Temporal knowledge graphs (TKGs) capture relationships and entities evolving over time, making event prediction a challenging task due to the complex temporal and relational dynamics. In this work, we propose BiCoTime, a novel model using bicomplex embeddings to represent entities, relations, and time. While quaternions capture asymmetric relations through non-commutativity, bicomplex numbers provide a commutative algebraic structure, ideal for modeling both symmetric and asymmetric relations. Unlike quaternions, bicomplex embeddings maintain interpretability in symmetric relations while preserving key algebraic properties like distributivity. Temporal rotations further enhance BiCoTime's ability to model the interaction between relations and time, capturing how entities and relationships evolve. This combination of bicomplex embeddings and temporal rotations ensures a more interpretable and accurate modeling of TKGs. Our experiments show that TBiComR achieved a 21% improvement in Mean Reciprocal Rank (MRR) on the ICEWS14 dataset, which emphasizes time points, and a 15% improvement on the YAGO11k dataset, which focuses on time spans. The choice of bicomplex numbers balances computational complexity and expressive power, offering efficient training and better predictive performance compared to models using quaternions or octonions.
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来源期刊
Knowledge-Based Systems
Knowledge-Based Systems 工程技术-计算机:人工智能
CiteScore
14.80
自引率
12.50%
发文量
1245
审稿时长
7.8 months
期刊介绍: Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.
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