8 results about "Name disambiguation" patented technology

The present invention provides a method for disambiguating personal names based on multi-relationship deep retrieval text matching, which relates to the technical field of personal name disambiguation. The present invention obtains cross-modal data related to enterprises and people, performs data alignment and data fusion on the cross-modal data through an entity alignment algorithm to form a person-enterprise structured data set, generates semantic vectors by establishing a multi-relationship deep retrieval model based on a pre-trained language model, calculates semantic similarity according to the semantic vectors, and generates a personal embedding vector according to the co-occurrence frequency and spatio-temporal correlation features calculated from the person-enterprise structured data set. An anti-disambiguation recognition model is established based on an adversarial neural network, personal name disambiguation is performed according to the personal embedding vector, a structure update model is established through a graph attention network to update the graph structure in real time, the confidence levels of semantic similarity and co-occurrence frequency are calculated according to Bayes' theorem, and the weights of semantic similarity and co-occurrence frequency are updated according to the confidence levels.

The present invention discloses a method for name disambiguation of paper data based on graph neural networks. This algorithm takes each paper as a node of a heterogeneous network, establishes edges through the strong correlation between paper attribute features, and uses an unsupervised graph autoencoder to learn the representation vector of each paper. At the same time, a hierarchical attention mechanism network is also adopted to enhance the vector representation of the paper. Finally, the hierarchical clustering algorithm is used to achieve name disambiguation for authors with the same name. Compared with traditional methods, the present invention uses graph neural networks to represent the nodes in the heterogeneous network, which can make full use of the correlation information between nodes and improve the accuracy of disambiguation. The present invention uses an unsupervised graph autoencoder to learn the representation vector of the paper, avoiding the problem of requiring a large amount of labeled data in traditional disambiguation methods. The present invention adopts a hierarchical attention mechanism network to learn the weight relationship between nodes and meta-paths, further enhancing the vector representation of the paper and the accuracy of disambiguation.

The application provides an improved same-name disambiguation method and device based on cross-source cross error correction, comprising: obtaining a cross-data-source data set; constructing an initial matching model, comparing the similarity of authors associated with a paper in an internal academic graph and authors associated with the paper in an external academic graph, dividing the cross-data-source data set to obtain a confidence set, an unconfidence set and a fuzzy set; obtaining first data in the fuzzy set, converting the hard label of the first data into a soft label, and generating a first target loss function according to the soft label; obtaining second data in the confidence set and third data in the unconfidence set, and performing cross error correction on the second and third data by using a preset cross error correction method to generate a second target loss function; training the initial matching model according to the first and second target loss functions to obtain an enhanced target matching model, and correcting a same-name disambiguation result by using the enhanced target matching model.

This invention provides a method, application, and electronic device for constructing an author name disambiguation model. First, a name block and an initial training set consisting of entity pair training samples are constructed. An author name disambiguation baseline model is trained based on the initial training set. The baseline model is then used to calculate the global feature importance vector for each entity pair training sample through feature attribution calculation. The score for each entity pair training sample is calculated based on the global feature importance vector and a non-empty indicator vector, and a training set is selected. The baseline model is then retrained using the selected training set to obtain the author name disambiguation model. The objective loss function of the author name disambiguation model includes classification loss and attribution prior regularization, improving model interpretability, reducing information loss due to metadata sparsity, suppressing model attention drift, and being compatible with various model structures. It has low engineering modification costs and can be widely applied to name disambiguation scenarios in various academic data processing systems.

The invention discloses a paper data processing method and system based on semantic similarity, the paper data processing method is suitable for duplication name disambiguation of paper authors, the paper data processing method comprises: obtaining comparison information of a target author, the comparison information comprising name information of the target author; obtaining a paper set of the name information, and extracting an author set and a unit set of each paper in the paper set; comparing whether the unit sets of any two papers are consistent or not, if the unit sets of the two papers are consistent, comparing the similarity of the papers, if the similarity of the papers is smaller than a first threshold value, comparing the coincidence rate of author sets, and if the coincidence rate of the author sets is smaller than a third threshold value, extracting keywords in the papers, and carrying out keyword semantic similarity comparative analysis; and if the semantic similarity of the keywords is smaller than a second threshold value, marking different labels on the name information of the two papers. According to the method, the duplication name disambiguation of the paper author can be realized.

The invention relates to the technical field of name disambiguation, and discloses a name disambiguation method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a to-be-disambiguated name data set, and forming a pairwise-combined name combination according to names; acquiring industry information of each name in the name combination; setting the weight of each preset dimension according to the industry information; calculating a dimension value of the name combination in a preset dimension, and performing weighted summation according to the dimension value of the preset dimension and the weight to obtain a weighted score value; judging whether the weighted score value is greater than a preset weighted score value or not; and affirming the names in the name combinations with the weighted score values greater than a preset weighted score value as the same name. The method has the beneficial effects that the name disambiguation efficiency and accuracy are remarkably improved, the problem of identity confusion of natural persons among different enterprises is effectively solved, and the storage overhead and the calculation complexity are greatly reduced.

The invention discloses a name disambiguation method based on deep semantic representation and a dynamic matching strategy, and belongs to the technical field of name disambiguation, and the method comprises the steps: obtaining and processing to-be-disambiguated name reference item data, and generating to-be-disambiguated name reference item data of a unified view; constructing a name disambiguation depth semantic capture model based on artificial intelligence, analyzing the to-be-disambiguated name reference item data, and determining to-be-disambiguated name depth semantic representation data; and dynamically matching the candidate cluster according to the deep semantic representation data of the name to be disambiguated, calculating a dynamic matching score between the deep semantic representation data of the name to be disambiguated and the candidate cluster, and realizing name disambiguation according to the dynamic matching score. According to the invention, the problem that the accuracy and efficiency of name disambiguation are reduced because name disambiguation cannot be realized based on deep semantic representation and dynamic matching in the prior art is solved. According to the method, name disambiguation can be realized based on deep semantic representation and dynamic matching, and the accuracy and efficiency of name disambiguation are effectively improved.

The application discloses a person name disambiguation method and device based on an equity relationship graph and personnel information, and comprises the following steps: reading enterprise business data, resume data and personnel position data; performing resume matching and resume feature similarity calculation on the read resume data; performing connected component segmentation on the constructed equity relationship graph and further judging whether the read natural person node pair to be compared belongs to the same connected component; if yes, performing natural person similarity calculation based on the common neighbors in the equity relationship graph of the natural person node pair to be compared and the subgraph structure similarity and the resume similarity to perform person name disambiguation; and if not, performing person name disambiguation based on the personnel position data, the natural person position enterprise senior manager homonym proportion to be compared and the resume similarity calculation. The application adopts a person name disambiguation method based on a partial attribute equity relationship graph, more effective information is applied in the person name disambiguation work in a financial graph, the process is simplified, and the calculation efficiency is improved.