Open knowledge graph completion with negative-aware representation learning and multi-source reliability inference

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.