Augmented Intelligence of Things empowered by knowledge graph drives cognitive intelligence for smart enterprise management systems (EMS). Knowledge fusion technology can effectively integrate knowledge from different sources, thereby improving the accuracy and richness of the knowledge graph, which is of great significance to the sustainable development of smart EMS. Traditional machine learning methods on graphs face challenges in the fusion of complex and multirelational enterprise knowledge graphs due to inherent defects in relation semantic and local structure information capturing. In order to break through these limitations and improve EMS knowledge graphs, we propose tensor-based graph attention networks for multirelational graph representation learning (MR-GAT), and apply it to the critical tasks in knowledge fusion: Entity and relation alignment. Specifically, we innovatively adopt tensor operations to adequately model the interactions between entities and relations in EMS knowledge graph to learn more accurate representations. Additionally, we propose a relation attention mechanism, which focuses on assigning weights in the process of aggregating local semantic information for relation learning in an EMS knowledge graph. Furthermore, we develop a joint entity and relation alignment framework by utilizing the proposed multirelational graph attention networks to improve the accuracy of knowledge fusion. Experimental evaluations on three datasets present that the proposed approach outperforms the baseline models by about 1.4% on average in terms of the mean reciprocal rank metric, which demonstrates the superior ability of the proposed MR-GAT in representation learning for knowledge fusion in smart EMS.

中文翻译：

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用于智能企业系统知识融合的多关系张量图注意力网络


知识图谱赋能的物联网增强智能驱动智能企业管理系统 (EMS) 的认知智能。知识融合技术可以有效整合不同来源的知识，从而提高知识图谱的准确性和丰富性，对智能EMS的可持续发展具有重要意义。由于关系语义和局部结构信息捕获的固有缺陷，传统的图机器学习方法在复杂、多关系的企业知识图的融合方面面临挑战。为了突破这些限制并改进EMS知识图，我们提出了基于张量的图注意力网络用于多关系图表示学习（MR-GAT），并将其应用于知识融合中的关键任务：实体和关系对齐。具体来说，我们创新性地采用张量运算来充分建模EMS知识图谱中实体和关系之间的交互，以学习更准确的表示。此外，我们提出了一种关系注意机制，该机制侧重于在聚合局部语义信息以进行 EMS 知识图谱中的关系学习的过程中分配权重。此外，我们利用所提出的多关系图注意网络开发了联合实体和关系对齐框架，以提高知识融合的准确性。对三个数据集的实验评估表明，所提出的方法在平均倒数排名指标方面平均优于基线模型约 1.4%，这证明了所提出的 MR-GAT 在智能 EMS 知识融合的表示学习中的卓越能力。