Guo Qinglang, Liao Yong, Li Zhe, Lin Hui, Liang Shenglin
School of Cyber Science and Technology, University of Science and Technology of China, Heifei 230027, China.
National Engineering Research Center for Public Safety Risk Perception and Control by Big Data (RPP), CETC Academy of Electronics and Information Technology Group Co., Ltd., China Academic of Electronics and Information Technology, Beijing 100041, China.
Entropy (Basel). 2023 Oct 21;25(10):1472. doi: 10.3390/e25101472.
Link prediction remains paramount in knowledge graph embedding (KGE), aiming to discern obscured or non-manifest relationships within a given knowledge graph (KG). Despite the critical nature of this endeavor, contemporary methodologies grapple with notable constraints, predominantly in terms of computational overhead and the intricacy of encapsulating multifaceted relationships. This paper introduces a sophisticated approach that amalgamates convolutional operators with pertinent graph structural information. By meticulously integrating information pertinent to entities and their immediate relational neighbors, we enhance the performance of the convolutional model, culminating in an averaged embedding ensuing from the convolution across entities and their proximal nodes. Significantly, our methodology presents a distinctive avenue, facilitating the inclusion of edge-specific data into the convolutional model's input, thus endowing users with the latitude to calibrate the model's architecture and parameters congruent with their specific dataset. Empirical evaluations underscore the ascendancy of our proposition over extant convolution-based link prediction benchmarks, particularly evident across the FB15k, WN18, and YAGO3-10 datasets. The primary objective of this research lies in forging KGE link prediction methodologies imbued with heightened efficiency and adeptness, thereby addressing salient challenges inherent to real-world applications.
在知识图谱嵌入（KGE）中，链接预测仍然至关重要，旨在识别给定知识图谱（KG）中隐藏的或不明显的关系。尽管这项工作至关重要，但当代方法仍面临显著限制，主要体现在计算开销以及封装多方面关系的复杂性方面。本文介绍了一种复杂的方法，该方法将卷积算子与相关的图结构信息相结合。通过精心整合与实体及其直接关系邻居相关的信息，我们提高了卷积模型的性能，最终得到了实体及其近端节点卷积后的平均嵌入。值得注意的是，我们的方法提供了一条独特的途径，便于将特定于边的数据纳入卷积模型的输入，从而赋予用户根据其特定数据集校准模型架构和参数的自由度。实证评估强调了我们的提议相对于现有基于卷积的链接预测基准的优势，在FB15k、WN18和YAGO3 - 10数据集上尤为明显。本研究的主要目标在于打造具有更高效率和适应性的KGE链接预测方法，从而应对现实世界应用中固有的突出挑战。
