Multi-source data fusion is essential for building smart cities by providing a comprehensive and holistic understanding of urban environments. Specifically, smart city-oriented knowledge graphs (KGs) require supplementary information from other open sources to increase their completeness, thus better supporting downstream tasks for smart cities. Nevertheless, existing open knowledge graph completion (KGC) approaches often overlook source quality assessment and fail to fully utilize prior knowledge, which tend to yield less satisfying results. To fill in these gaps, in this work, we propose a new open KGC method with negative-aware representation learning and multi-source reliability inference, i.e., Nari, which can effectively integrate the multi-source data concerning sustainable cities, providing reliable knowledge for downstream tasks. Specifically, we first train a graph neural network based encoder with a novel negative sampling strategy to better characterize prior knowledge in KG, and then identify new facts based on the learned prior knowledge and source reliability. The experiments on both general benchmark and waterlogging benchmark pertaining to sustainable cities demonstrate the effectiveness and wide applicability of Nari.

中文翻译：

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具有负感知表示学习和多源可靠性推理的开放知识图谱补全


多源数据融合通过提供对城市环境的全面和整体理解，对于构建智慧城市至关重要。具体来说，面向智慧城市的知识图谱 （KG） 需要来自其他开源的补充信息以提高其完整性，从而更好地支持智慧城市的下游任务。然而，现有的开放知识图谱完成 （KGC） 方法往往忽视源质量评估，未能充分利用先验知识，这往往会产生不太令人满意的结果。为了填补这些空白，在这项工作中，我们提出了一种新的具有负感知表示学习和多源可靠性推理的开放 KGC 方法，即 Nari，它可以有效地整合有关可持续城市的多源数据，为下游任务提供可靠的知识。具体来说，我们首先使用一种新的负采样策略训练一个基于图神经网络的编码器，以更好地描述 KG 中的先验知识，然后根据学到的先验知识和来源可靠性识别新事实。与可持续城市相关的通用基准和内涝基准的实验证明了 Nari 的有效性和广泛适用性。