Abstract: The present application discloses a method for training a classification model, a classification method, an apparatus and a device. A specific implementation is: acquiring behavior information of multiple users and personal basic information of the multiple users; where categories of at least part of users of the multiple users are known; inputting the personal basic information of the multiple users into a classification model to be trained to obtain feature information of the multiple users and predicted categories of users with known categories; and training the classification model to be trained according to the behavior information of the multiple users, the feature information of the multiple users, the predicted categories of the users with the known categories, and real categories of the users with the known categories, to obtain a trained classification model. The user categories determined by using the classification model are more accurate.

Abstract: Embodiments of the present disclosure provide a data mining system, a data mining method, and a storage medium. The data mining system includes a transfer device, a first trusted execution space and a second trusted execution space. The transfer device is configured to receive a data calling request of the second trusted execution space, obtain data to be called from the first trusted execution space according to the data calling request, and provide the data to be called to the second trusted execution space, so as to perform data mining based on the data to be called and the mining-related data to obtain a data mining result and to provide the data mining result to a device of the data user.

Abstract: A traffic pattern prediction method, device, server, system, and a computer-readable medium are provided.

Abstract: Semantic errors in a natural language text document are automatically detected by matching sentences in the document with stored ontology-based extraction rules that express both logically correct and logically incorrect relationships between the classes and properties of an ontology for a predefined knowledge domain of relevance to the natural language text document. The matching identifies logically correct and incorrect statements in the document which may be used for various applications such as automatic grading.

Abstract: Semantic errors in a natural language text document are automatically detected by matching sentences in the document with stored ontology-based extraction rules that express both logically correct and logically incorrect relationships between the classes and properties of an ontology for a predefined knowledge domain of relevance to the natural language text document. The matching identifies logically correct and incorrect statements in the document which may be used for various applications such as automatic grading.