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Distantly supervised relation extraction aims to obtain relational facts from unstructured texts. Although distant supervision can automatically generate labeled training instances, it inevitably suffers from the wrong-label problem. Most of the current work is based on the bag-level for solving the noise problem, where a bag is composed of multiple sentences containing mentions of the same entity pair. However, previous studies mostly represent sentences from a single perspective, wherein insufficient modeling of global information restricts the effectiveness of denoising. In this study, we propose a novel distantly supervised relation extraction approach that incorporates the global contextual information of sentences to guide the denoising process and generate an effective bag-level representation. Simultaneously, knowledge-aware word embeddings were generated to enrich sentence-level representations by introducing both structured knowledge from external knowledge graphs and semantic knowledge from the corpus. The experimental results demonstrate that our proposed approach outperforms state-of-the-art methods on both versions of the large-scale benchmark New York Times dataset. In addition, the differences between the two versions of the dataset were investigated through further comparative experiments. [ABSTRACT FROM AUTHOR]

Correlated time series refer to multiple time series which are recorded simultaneously to monitor the changing of multiple observations in a whole system. Correlated time series prediction plays a significant role in many real-world applications to help people make reasonable decisions. Yet it is very challenging, because different from single time series, correlated time series show both intra-sequence temporal dependencies and inter-sequence spatial dependencies. In addition, correlated time series are also affected by external factors in actual scenarios. Although RNNs have been proved to be effective on sequential data modeling, existing related works only focus on sequential patterns in a single time series, failing to comprehensively consider the inter-dependencies among multiple time series, which is essential for correlated time series prediction. In this paper, we propose a novel variant of LSTM, named CTS-LSTM, to collectively forecast correlated time series. Specifically, spatial and temporal correlations are explicitly modeled and respectively maintained in cells to capture the complex non-linear patterns in correlated time series. A general interface for handling external factors is further designed to enhance forecasting performance of the model. Experiments are conducted on two types of real-world datasets, viz., civil aviation passenger demand data and air quality data. And our CTS-LSTM achieves at least 9.0%, 16.5% and 21.3% lower RMSE, MAE and MAPE compared to the state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

In intelligent transportation systems (ITS), deploying fine-grained sensors to continuously collect spatial–temporal traffic data is important but impractical due to the expensive cost. Fortunately, spatial–temporal kriging methods bring advanced solutions for interpolating traffic data for locations without sensors, but they still have the following two drawbacks: (1) The widely adopted predefined and adaptive graphs are either inflexible or limited to transductive learning. (2) The sampling strategies to support inductive learning on new graphs result in losing important partial information. To overcome the above issues, in this paper, we propose an Inductive and Adaptive Graph Convolution Networks (IAGCN) for spatial–temporal traffic data kriging in an inductive manner. Specifically, we propose an adaptive graph constructor to model the hidden spatial relation of nodes and learn the spatial dependency that the predefined graph structure cannot capture. It can work inductively and does not require retraining when introducing new nodes. Additionally, we design a framework that integrates inductive graph convolution and temporal convolution to simultaneously capture the complex spatial–temporal dependencies of traffic data. Finally, to address the information loss issue caused by random sampling, we design a predicted-based constraint task that perceives and utilizes the history information of all observed sensors to predict the current data, as well as approach the result of the interpolation and prediction. Experiments on four real-world datasets show that IAGCN outperforms the state-of-the-art baselines. [ABSTRACT FROM AUTHOR]