Abstract: A citation network graph representation learning system and method based on multi-view contrastive learning is provided. The citation network graph representation learning system involved in the present application comprises: a sample construction module, which is configured to construct a corresponding negative sample based on an original graph; a graph enhancement module, which is configured to obtain a positive sample graph and a negative sample graph; a fusion module, which is configured to obtain a consensus representation of the positive sample graph and the negative sample graph by means of a cross view concentration fusion layer; a mutual information estimation module, which is configured to compare learning representations of positive sample pairs and negative sample pairs by means of a discriminator; and a hard sample mining module, which is configured to represent the consistency between the negative sample pairs according to a pre-calculated affinity vector, and select and reserve nodes.

Abstract: This application discloses a text mining method based on artificial intelligence performed by a computer device. This application includes: obtaining domain candidate term features corresponding to domain candidate terms; obtaining term quality scores corresponding to the domain candidate terms according to the domain candidate term features; determining a new term from the domain candidate terms according to the term quality scores corresponding to the domain candidate terms; obtaining an associated text according to the new term; and determining a domain seed term as a domain new term in response to determining according to the associated text that the domain seed term satisfies a domain new term mining condition. By this application, new terms can be automatically selected from domain candidate terms based on a machine learning algorithm, thereby reducing manpower costs and well adapting to the rapid emergence of special new terms in the Internet era.

Abstract: This disclosure relates to a method and an apparatus for mapping wireless hotspots and points of interest (POIs), a computer-readable storage medium, and a computer device. The method includes: obtaining sniffing records, each of the sniffing records including data of wireless hotspots sniffed by sniffing devices; determining sniffing device overlap degrees between the wireless hotspots according to the sniffing records; determining, according to distances between the wireless hotspots and POIs, initial mapping probabilities between the wireless hotspots and the POIs; performing iterative propagation among the initial mapping probabilities based on the sniffing device overlap degrees, and obtaining target mapping probabilities between the wireless hotspots and the POIs when the iteration ends; and establishing a mapping between the wireless hotspots and the POIs according to the target mapping probabilities.

Abstract: This disclosure relates to a method for matching a wireless hotspot and a point of interest. The method may include performing semantic normalization preprocessing on a wireless hotspot name and a point of interest name respectively. The method may further include determining a first similarity between the preprocessed wireless hotspot name and the preprocessed point of interest name according to a matching result of consecutive common strings of the preprocessed wireless hotspot name and the preprocessed point of interest name. The method may further include determining a second similarity between the preprocessed wireless hotspot name and the preprocessed point of interest name according to a length difference between lengths of the preprocessed wireless hotspot name and the preprocessed point of interest name. The method may further include determining a matching result of the wireless hotspot and the point of interest according to the first similarity or the second similarity.