The graph structures generated by natural or simple heuristics often fail to represent the spatial correlations influenced by complex factors. Therefore, introducing graph structure learning (GSL) can enhance the graph neural network-based spatial interpolation models. However, the input features of the GSL module are systematically unbalanced in spatial interpolation tasks. For example, many natural variables follow Gaussian- or gamma-distribution, and sensor spatial distributions are generally uneven. Thus, the GSL module must systematically incorporate corresponding solutions to avoid negatively impacting its generalization ability and degrading model performance. Our proposed model utilizes two encoders to embed feature deviations of node readings and centroid distance from preset distributions, respectively. Notably, these encoders are jointly optimized with other model components, and their generalization ability is improved through an adaptively adjustable information bottleneck. Consequently, the GSL module can offer a more robust graph structure by explicitly perceiving feature deviations in the input. Experimental results demonstrate that our model outperforms existing state-of-the-art baselines across multiple real-world datasets with diverse characteristics.

Recent machine learning models find a widespread use whether in respect of data mining and forecasting or in the classification domain. However, real-world situations comprise complex estimation tasks that carry a certain semantic load and bring a certain degree of fuzziness with them. This is a fuzziness which humans, due to their common sense knowledge and their personal experience, can easily understand by linking the underlying concepts together, while machines may from scratch not. A vast amount of both training data and time are necessary in order for a computational model to be capable of learning such kind of relations and adapting to new situations. In this work, we show that letting explicit semantic knowledge flow into a predictive model leads to an improved performance with regard to training time, accuracy and robustness. In particular, we propose adding an auxiliary semantic layer to the model, whose role is to provide it with information about the semantic interrelation of the treated classes creating in this way shortcuts and saving valuable training time while improving its quality at the same time. We explore several versions of our approach and we illustrate their functionality in a semantic location prediction scenario using 2 different real-world datasets.

Proactive mobile applications and services have the advantage of providing their users with timely and customized solutions improving in this way the human-machine interaction. For this reason, Location Based Services (LBS) rely increasingly on predictive models that estimate how likely it is for a user to visit a certain location. Recently, Artificial Neural Networks, and especially recurrent architectures such as the LSTMs, have shown a particularly good performance in this field. In this work, we extend a LSTM network by applying Sequence to Sequence (Seq2Seq) learning on human semantic trajectories. In particular, we explore whether and to what extent Attention-based Seq2Seq learning in combination with neural networks can contribute to improving the accuracy in a location prediction scenario. We compare the performance of our framework with the performance of a standard LSTM, a semantic trajectory tree-based approach and a probabilistic graph of first and higher order on two different real-world datasets. It can be shown that Sequence to Sequence learning may well be used to model semantic trajectories and predict future human movement patterns.

Article Free Access Share on Assistant for an information database Authors: Michael Früchtl SAP AG Walldorf, Human Resources Management Systems, P.O.Box 1461, 69185 Walldorf, Germany SAP AG Walldorf, Human Resources Management Systems, P.O.Box 1461, 69185 Walldorf, GermanyView Profile , Jürgen Kreuziger SAP AG Walldorf, R/3 Services SAP AG Walldorf, R/3 ServicesView Profile , Michael Beigl Telecooperation Office, Institute of Telematics, University of Karlsruhe, Vincenz-Priessnitz-Strasse 1, 76131 Karlsruhe, Germany Telecooperation Office, Institute of Telematics, University of Karlsruhe, Vincenz-Priessnitz-Strasse 1, 76131 Karlsruhe, GermanyView Profile Authors Info & Claims CIKM '97: Proceedings of the sixth international conference on Information and knowledge managementJanuary 1997 Pages 230–237https://doi.org/10.1145/266714.266903Online:01 January 1997Publication History 1citation237DownloadsMetricsTotal Citations1Total Downloads237Last 12 Months8Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF